I offer two potential diagnoses of the behavioral norms governing post‐truth politics by comparing the view of language, communication, and truth‐telling put forward by David Lewis (extended by game theorists), and John Searle. My first goal is to specify the different ways in which Lewis, and game theorists more generally, in contrast to Searle (in the company of Paul Grice and Jurgen Habermas), go about explaining the normativity of truthfulness within a linguistic community. The main difference is that (...) for Lewis and game theorists, “truthful” signaling follows from an align- ment of interests, and deception follows from mixed motives leading to the calculation that sending false information is better for oneself. Following in the Enlightenment tradition, Searle argues that practical reasoning, which involves mastery of at least one language, requires that actors intend to communicate. This intention includes constraining the content of statements to uphold veracity conditions. After distinguishing between these two accounts, I will artic- ulate the implications for explaining, and even informing actions, constitutive of post‐truth politics. I argue that the strategic view of communication is suffi- cient neither to model everyday conversation nor to reflect a public sphere useful for democratic govern- ment. Both the pedagogy of strategic communication as cheap talk, and its concordance with new digital information technologies, challenge norms of truthful- ness that underlie modern institutions essential to an effective public sphere. (shrink)

How do conventions of communication emerge? How do sounds or gestures take on a semantic meaning, and how do pragmatic conventions emerge regarding the passing of adequate, reliable, and relevant information? My colleagues and I have attempted in earlier work to extend spatialized game theory to questions of semantics. Agent-based simulations indicate that simple signaling systems emerge fairly naturally on the basis of individual information maximization in environments of wandering food sources and predators. Simple signaling emerges by means (...) of any of various forms of updating on the behavior of immediate neighbors: imitation, localized genetic algorithms, and partial training in neural nets. Here the goal is to apply similar techniques to questions of pragmatics. The motivating idea is the same: the idea that important aspects of pragmatics, like important aspects of semantics, may fall out as a natural results of information maximization in informational networks. The attempt below is to simulate fundamental elements of the Gricean picture: in particular, to show within networks of very simple agents the emergence of behavior in accord with the Gricean maxims. What these simulations suggest is that important features of pragmatics, like important aspects of semantics, don't have to be added in a theory of informational networks. They come for free. (shrink)

This article argues that various deviations from the basic principles of the scientific ethos – primarily the appearance of pseudoscience in scientific communities – can be formulated and explained using specific models of game theory, such as the prisoner’s dilemma and the iterated prisoner’s dilemma. The article indirectly tackles the deontology of scientific work as well, in which it is assumed that there is no room for moral skepticism, let alone moral anti-realism, in the ethics of scientific communities. (...) Namely, on the basis of the generally accepted dictum of scientific endeavor as the pursuit of knowledge exclusively for knowledge’s sake, scientifically »right« behavior is seen to be clearly defined and distinguishable from scientifically »wrong« behavior. After elucidating the basic principles of game theory, the article illustrates – by using imaginary and real cases, as well as some views from the philosophyof biology (the units of selection debate) – how this sort of reasoning could be applied in an analysis of the functioning of science. (shrink)

When game theory was introduced to biology, the components of classic game theory models were replaced with elements more befitting evolutionary phenomena. The actions of intelligent agents are replaced by phenotypic traits; utility is replaced by fitness; rational deliberation is replaced by natural selection. In this paper, I argue that this classic conception of comprehensive reapplication is misleading, for it overemphasizes the discontinuity between human behavior and evolved traits. Explicitly considering the representational roles of evolutionary (...) class='Hi'>game theory brings to attention neglected areas of overlap, as well as a range of evolutionary possibilities that are often overlooked. The clarifications this analysis provides are well-illustrated by—and particularly valuable for—game theoretic treatments of the evolution of social behavior. (shrink)

Universal social institutions, such as marriage, commons management and property, have emerged independently in radically different cultures. This requires explanation. As Boyer and Petersen point out ‘in a purely localist framework would have to constitute massively improbable coincidences’ . According to Boyer and Petersen, those institutions emerged naturally out of genetically wired behavioural dispositions, such as marriage out of mating strategies and borders out of territorial behaviour. While I agree with Boyer and Petersen that ‘unnatural’ institutions cannot thrive, this (...) one-sided explanation of universal social institutions in terms of genetic human nature is unsatisfactory. Drawing on the literature on multi-level selection and gene-culture coevolution, I argue that universal social institutions are first and foremost the products of cultural selection. They occupy fitness peaks in the landscape of cultural possibilities, much in the same way that biological adaptations occupy fitness peaks in the landscape of biological possibilities. To show this, I use game-theory. By modelling the domains of social interaction in which marriage, commons management, and property emerged as Prisoner’s dilemma situations, it becomes clear how an institutional framework allows the group to move to an interactive equilibrium with a larger payoff. Institutions do so by incentivising all parties to adopt a cooperative strategy. They are culturally selected ways of optimising genetically constrained domains of human social interaction. (shrink)

We perform an experimental investigation using a dictator game in which individuals must make a moral decision —to give or not to give an amount of money to poor people in the Third World. A questionnaire in which the subjects are asked about the reasons for their decision shows that, at least in this case, moral motivations carry a heavy weight in the decision: the majority of dictators give the money for reasons of a consequentialist nature. Based on the (...) results presented here and of other analogous experiments, we conclude that dicator behavior can be understood in terms of moral distance rather than social distance and that it systematically deviates from the egoism assumption in economic models and game theory. (shrink)

Developments that suggest the universe is full of life make the Fermi paradox increasingly pressing, but our search for an extraterrestrial technological civilization (“ETC”) is handicapped by our ignorance of its probable nature and behavior. This paper offers a way around this problem by drawing on information theoretical concepts, including game theory and Bayesian probability. It argues that, whatever its ultimate goals, an ETC would have the same instrumental goals as other intelligent agents. Generically, these are self-preservation and (...) the acquisition of resources. For an advanced ETC, these generic goals imply the objectives of removing existential threats and acquiring strategic and non-strategic information. Since the most problematic existential threats for any ETC would be from other civilizations and planets hosting such civilizations would also be copious sources of information, these objectives would lead it to gather information from these locations and to protect itself against them. This dual-goals hypothesis resolves the Fermi paradox, redirects the search for extraterrestrial intelligence and makes testable predictions regarding ETC’s behavior on Earth if it is here. (shrink)

Climate change mitigation has become a paradigm case both for externalities in general and for the game-theoretic model of the Tragedy of the Commons (ToC) in particular. This situation is worrying, as we have reasons to suspect that some models in the social sciences are apt to be performative to the extent that they can become self-fulfilling prophecies. Framing climate change mitigation as a hardly solvable coordination problem may force us into a worse situation, by changing real-world behaviour to (...) fit our model, rather than the other way around. But while this problem of the performativity of the ToC has been noted in a recent paper in this journal by Matthew Kopec, his proposed strategies for dealing with their self-fulfilling nature fall short of providing an adequate solution. Instead of relying on the idea that modelling assumptions are always strictly speaking false, this paper shows that the problem may be better framed as a problem of underdetermination between competing explanations. Our goal here is to provide a framework for choosing between this set of competing models that allows us to avoid a ‘Russian Roulette’-like situation in which we gamble with existential risk. (shrink)

The use of evolutionary game theory to explain the evolution of human norms and the behavior of humans who act according to those norms is widespread. Both the aims and motivation for its use are clearly articulated by Harms and Skyrms (2008) in the following passage: "A good theory of evolution of norms might start by explaining the evolution of altruism in Prisoner’s Dilemma, of Stag Hunting, and of the equal split in the symmetric bargaining game. (...) These are not well-explained by classical game theory based on rational choice. From a technical point of view, they present different theoretical challenges. In the bargaining game, there are an infinite number of equilibria with no principled (rational choice) way to select the cooperative one. In Stag Hunt there are only two, but the non-cooperative one is selected by risk-dominance. In Prisoner’s Dilemma the state of mutual cooperation is not a Nash equilibrium at all, and cooperation flies in the face of the rational-choice principle that one does not choose less rather than more. In contrast to rational choice theory, the most common tool of evolutionary game theory is the replicator dynamics, in which the propagation rate of each strategy is determined by its current payoffs. These dynamics have a rationale in both biological and cultural evolutionary modeling, and sometimes tell us things that rational choice theory does not." We agree with the first sentence in this quotation: a good theory about the behavior under norms ought to explain altruism in the Prisoner’s Dilemma (PD), playing Stag in Stag Hunt (SH), and offering equal splits in the symmetric Nash bargaining game (NB). We also agree with Harms and Skyrms about the difference in technical challenges each of these games poses. Finding a single mechanism, even one as broadly understood as evolution, that could solve these challenges en masse is no doubt a tall order. Nonetheless, in this paper, we present a single, simple, modification to SH, NB, and a general n-player PD that does just that: we introduce deontological autonomy into the models. (shrink)

We present a theory of cooperative-competitive intelligence (CCI), its measures, research program, and applications that stem from it. Within the framework of this theory, satisficing sub-optimal behavior is any behavior that does not promote a decrease in the prospective control of the functional action diversity/unpredictability (D/U) potential of the agent or team. This potential is defined as the entropy measure in multiple, context-dependent dimensions. We define the satisficing interval of behaviors as CCI. In order to manifest itself at (...) individual or team level, this capacity harnesses properties such as degeneracy, pleiotropy (pluri-potentiality), synergies, and metastability. Intelligence is embodied because intelligent behavior is deeply dependent on body functionalities, defined as entropy measures. We base our theory on three principles: (a) relativity of functional entropy/ information in agent (team)-environment systems, (b) tendency toward the satisficing level of D/U potential, and (c) tendency toward the non-decreasing D/U potential. The conjunction of these three principles provides existence of sub-optimal behaviors associated with CCI. First, we deal with the problem of how to reduce multidimensional behavior to a concept that accounts for the vast set of scenarios in which CCI is manifested. Secondly, we define and discuss the three interacting principles that underpin CCI behavior as well as providing an outline for a future CCI research program supported by agent￾based modeling and empirical research. Finally, we provide some preliminary practical issues that stem from the theory. (shrink)

The evidence of laboratory experiments of behavioral economists shows that individuals behave reciprocally. These data put into question the pure self-interest thesis of human motivation of the homo oeconomicus model and call for alternative models. Focusing on the explanation of reciprocal behavior in Trust Games, this article proposes two directions that economists and other social scientists might want to consider in order to establish a more solid foundation for economic theory. First, it presents models that economic theorists developed to (...) explain the laboratory evidence of reciprocal behavior. It highlights that all of these models subscribe to the Humean view that desires are at the source of any human motivation and suggests an alternative Kantian model where reasons have the capacity to motivate human action. Second, it emphasizes that a supplementary examination of the social background conditions would illuminate the analysis of the findings because of the connection between the 'local' and society-wide demands of reciprocity. (shrink)

This dissertation summarises a research path aimed at fostering the use of Cognitive Architectures in Serious Games research field. Cognitive Architectures are an embodiment of scientific hypotheses and theories aimed at capturing the mechanisms of cognition that are considered consistent over time and independent of specific tasks or domains. The theoretical approaches provided by the research in computational cognitive modelling have been used to formalise a methodological framework to guide researchers and experts in the game-based education sector in designing, (...) implementing, and evaluating Serious Games. The investigation of cognitive processes involved during the game experience represents the fundamental step of the pro- posed approach. Two different case studies are described to discuss the possible use of the suggested framework. In the first case study, the aim was to design a modified version of the Tetris game with the intention of making the game more effective in training the visual-spatial skill called mental rotation. In the second scenario, the frame- work was used as a basis for creating an innovative persuasive game. This case study provides an example of adopting cognitive architectures for implementing a non-player character with human-like behaviour developed using targeted cognitive theories. (shrink)

We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis (...) that people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies. (shrink)

In the thesis I present a novel theory of semantic reference, which I call the coordination view of semantic reference. In chapter one I develop a novel theory of conventions, which I call the coordinating rule view of conventions. On the coordinating rule view, roughly, a convention is a rule which originates in a meta-coordination game and is adopted in order to deal with a series of future coordination games. This view is Lewisian in spirit, but rejects (...) Lewis’s view that a convention is a regularity in behaviour. In chapter two I use the coordinating rule view of conventions in order to define the coordination view of semantic reference. On the coordination view, roughly, a name N semantically refers to a particular o if, and only if, there is a convention to use N to speaker-refer to o. I also argue that a consideration of the coordination view of semantic reference makes it plain that Kripke’s causal theory has no non-trivial explanatory content. The causal chain that exists between a baptism and downstream use of a name comes about in virtue of the fact that knowledge of the content of a convention is typically causally acquired. This fact, however, is a mere triviality about conventions as such, not a revelation about reference. I also argue that non-Kripkean versions of causalism have little to recommend them. In chapter three I compare the coordination view of semantic reference with the traditional descriptivism of Russell and Frege. I defend the claim that traditional descriptivism is plainly false. This follows from the commitment of the traditional descriptivists to what I call eccentricity about names, i.e. the view that the descriptive condition which the utterer of the name associates with the name determines the semantic reference of the name. In fact, I claim that traditional descriptivism is so plainly false that we need to explain why Russell and Frege proposed it. I try to provide such an explanation. In chapter four I address various issues raised by the discussion in the previous three chapters. The main claim defended is that it is very strange that nothing like the coordination view of semantic reference features in discussion concerning semantic reference, given that the analogous view of sentence meaning, i.e. the view that sentence meaning is to be defined in terms of speaker-meaning and convention, has received a lot of attention. (shrink)

The purpose of this paper is to provide a detailed technical protocol analysis of chess masters' evaluative expertise, paying particular attention to the analysis of the structure of their memory process in evaluating foreseen possibilities in games of dynamic equilibrium. The paper has two purposes. First, to publish a results chapter from my DPhil thesis (in revised journal article form) attending to the measurement of foresight in chess masters' evaluation process, testing alternative theories of cognitive expertise in the domain of (...) chess; and second to provide a subset of the technical graphical analysis that corresponds to that measurement to preserve this protocol analysis for access in the academic domain for future studies of expert memory and foresight (e.g., Ericsson & Simon, 1993). The step-by-step protocol analysis consists of: (i) an introduction to foresight cognition as hypothesis testing, (ii) a theoretical review in the domain of chess masters' expertise according to the theoretical frameworks in that field purporting hypotheses relevant to chess masters' evaluative skill processes, and (iii) summary tables and non-parametric statistical analysis corroborating chunking theory frameworks of expert cognition (e.g., DeGroot, 1965; Newell & Simon, 1972; Gobet, 1998; Gobet et al., 2004), and refuting the alternative search-evaluation models (e.g., Holding & Reynolds, 1982). Moreover, the journal article espouses the preservation of the traditional protocol analysis method core to the field of expert cognition (DeGroot, 1969; Kotov, 1971). The full protocol analysis can be found in monograph form here on my SSRN profile in ‘The role of falsification in hypothesis testing’. It takes the form of a specialist population study (e.g., detailed case study work; Luria, 1987). Thus the outline consists of a short introduction, a theoretical methodological review discussing protocol analysis methods for specialist population studies in cognition (with particular attention to the preservation of protocol analysis methods for chess studies in cognition and expert memory/ with a fresh angle on the foresight process), and the full set of protocol analyses with corresponding problem behaviour graphs. A subset of the main results has been published elsewhere (e.g., Cowley & Byrne, 2004; Cowley 2006), receiving scientific and scientific journalistic acclaim (e.g., Nature Online News 2004). (shrink)

Morality is traditionally understood as comprised of two components: justice and mercy. The first component, justice, the universal component of the form, is frequently seen as foundational for any moral system – which poses a challenge of explaining the second component, mercy, the particular component of content. Kantian ethics provides an example of this approach. After formulating his universalist theory of ethics in the Groundwork of the metaphysics of morals and further developing it in the Critique of practical reason, (...) he attempts to use it in order to establish the morality of mercy in the Metaphysics of morals. Yet can universal morality of justice necessitate particular ethics of mercy? Using the example of competitive games, the relations between the ethics of justice and that of mercy are demonstrated, and it is shown that the former does not lead to the latter. Moreover, the universality of the rules of moral behavior can serve as a form for blatant brutality. Analyzing the characteristics of particular morality, we can conclude that physical humanity of the moral object, perceived as such by the subject, is a required condition for mercy. Removal of object's humanity is a necessary step toward an ethical system that allows cruelty – a system that can still be based on universal moral rules. Bhagavad Gītā, on the other hand, can be seen as an example of combining nīṣkāmakarma, the formal, universal ethics of desireless action, with a variety of particular motivations originating in the nature and social context of the moral agent. (shrink)

When does it make sense to act randomly? A persuasive argument from Bayesian decision theory legitimizes randomization essentially only in tie-breaking situations. Rational behaviour in humans, non-human animals, and artificial agents, however, often seems indeterminate, even random. Moreover, rationales for randomized acts have been offered in a number of disciplines, including game theory, experimental design, and machine learning. A common way of accommodating some of these observations is by appeal to a decision-maker’s bounded computational resources. Making this (...) suggestion both precise and compelling is surprisingly difficult. Toward this end, I propose two fundamental rationales for randomization, drawing upon diverse ideas and results from the wider theory of computation. The first unifies common intuitions in favour of randomization from the aforementioned disciplines. The second introduces a deep connection between randomization and memory: access to a randomizing device is provably helpful for an agent burdened with a finite memory. Aside from fit with ordinary intuitions about rational action, the two rationales also make sense of empirical observations in the biological world. Indeed, random behaviour emerges more or less where it should, according to the proposal. (shrink)

An important objection to signaling approaches to representation is that, if signaling behavior is driven by the maximization of usefulness, then signals will typically carry much more information about agent-dependent usefulness than about objective features of the world. This sort of considerations are sometimes taken to provide support for an anti-realist stance on representation itself. The author examines the game-theoretic version of this skeptical line of argument developed by Donald Hoffman and his colleagues. It is shown that their argument (...) only works under an extremely impoverished picture of the informational connections that hold between agent and world. In particular, it only works for cue-driven agents, in Kim Sterelny’s sense. In cases in which the agents’ understanding of what is useful results from combining pieces of information that reach them in different ways, and that complement one another, maximizing usefulness involves construing first a picture of agent-independent, objective matters of fact. (shrink)

As soon as you believe an imagination to be nonfictional, this imagination becomes your ontological theory of the reality. Your ontological theory (of the reality) can describe a system as the reality. However, actually this system is only a theory/conceptual-space/imagination/visual-imagery of yours, not the actual reality (i.e., the thing-in-itself). An ontological theory (of the reality) actually only describes your (subjective/mental) imagination/visual-imagery/conceptual-space. An ontological theory of the reality, is being described as a situation model (SM). There (...) is no way to prove/disprove that there is only one reality, or there are two realities (i.e., “subjective reality” and “objective reality”). So, every ontology talk/theory/imagination about the two realities is only a talk/theory/imagination – we will never know whether it is true or not. The conventionally-called “physical/objective reality/world” around my conventionally-called “physical/objective body” is actually a geometric mathematical model (being generated/mathematically-modeled by my brain) – it's actually a subset/component/part/element of my brain’s mind/consciousness/manifest-image. Our cosmos is an autonomous objective parallel computing automaton (aka state machine) which evolves by itself automatically/unintentionally – wave-particle duality and Heisenberg’s uncertainty principle can be explained under this SM of my brain. Each elementary particle (as a building block of our cosmos) is an autonomous mathematical entity itself (i.e., a thing in itself). Our cosmos has the same nature as a Game of Life system – both are autonomous objective parallel-computing automata. Cosmos (as a state machine) is indistinguishable from a digital simulation – my consciousness (as something nonphysical) is not cosmos (as a state machine). If we are happy to accept randomness/stochasticity, then it is obviously possible that all other worlds in the many-worlds interpretation (MWI) actually do not exist (objectively). As one metaphysical option, we can treat all other worlds as subjective only (even if they are actually objective). Under the context of this metaphysical option, we are only living in one world (i.e., the world we are currently living in; this world) – we are not living in many worlds at the same time parallelly. Under the context of this metaphysical option, there is only one possible future. The relationship among any number of elementary particles, is governed/described by Schrodinger equation. If (in theory) Schrodinger equation can’t be used to reliably forecast whether I will go to McDonald for dinner in this world (based on the current state of all elementary particles of the cosmos), then what can Schrodinger equation do? The conventionally-called “space” does not exist objectively. “Time” and “matter” are not physical. Consciousness is the subjective-form (aka quale) of the mathematical models (of the objective cosmos) which are intracorporeally/subjectively used by the control logic of a Turing machine’s program fatedly. A Turing machine’s mind/consciousness/manifest-image or deliberate decisions/choices should not be able to actually/objectively change/control/drive the (autonomous or fated) worldline of any elementary particle within this world (i.e., the world we are currently living in, under the context of MWI). Besides the Schrodinger equation (or another mathematical equation/function which is yet to be discovered) which is a valid/correct/factual causality of our cosmos/state-machine, every other causality (of our cosmos/state-machine) is either invalid/incorrect/counterfactual or can be proved by deductive inference based on the Schrodinger equation (or the aforementioned yet-to-be-discovered mathematical equation/function) only. Closed causality entails no causality. Consciousness plays no causal role (“epiphenomenalism”), or in other words, any cognitive/behavioural activity can in principle be carried out without consciousness (“conscious inessentialism”). If the “loop quantum gravity” theory is correct, then time/space does not actually/objectively exist in the objective-evolution of the objective cosmos, or in other words, we should not use the subjective/mental concept of “time”, “state” or “space” to describe/imagine the objective-evolution of our cosmos. (shrink)

As so often in philosophy, the title not only lays down the battle line but exposes the author’s biases and mistakes, since whether or not we can make sense of the language game ‘Seeing things as they are’ and whether it’s possible to have a ‘philosophical’ ‘theory of perception’ (which can only be about how the language of perception works), as opposed to a scientific one, which is a theory about how the brain works, are exactly the (...) issues. This is classic Searle—superb and probably at least as good as anyone else can produce, but lacking a full understanding of the fundamental insights of the later Wittgenstein and with no grasp of the two systems of thought framework, which could have made it brilliant. As in his previous work, Searle largely avoids scientism but there are frequent lapses and he does not grasp that the issues are always about language games, a failing he shares with nearly everyone. After providing a framework consisting of a Table of Intentionality based on the two systems of thought and thinking and decision research, I give a detailed analysis of the book. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019). (shrink)

In recent years, the human ability to reasoning about mental states of others in order to explain and predict their behavior has come to be a highly active area of research. Researchers from a wide range of fields { from biology and psychology through linguistics to game theory and logic{ contribute new ideas and results. This interdisciplinary workshop, collocated with the Thirteenth International Conference on Theoretical Aspects of Rationality and Knowledge (TARK XIII), aims to shed light on models (...) of social reasoning that take into account realistic resource bounds. People reason about other people's mental states in order to understand and predict the others' behavior. This capability to reason about others' knowledge, beliefs and intentions is often referred to as theory of mind. Idealized rational agents are capable of recursion in their social reasoning, and can reason about phenomena like common knowledge. Such idealized social reasoning has been modeled by modal logics such as epistemic logic and BDI (belief, desire, intention) logics and by epistemic game theory. However, in real-world situations, many people seem to lose track of such recursive social reasoning after only a few levels. The workshop provides a forum for researchers that attempt to analyze, understand and model how resource-bounded agents reason about other minds. (shrink)

In this paper, we examine how a research institution’s social structure and academic opinion leaders’ presence shaped the early adoption of a scientific innovation. Our case considers the early engagement of mathematical economists at the Cowles Commission with John von Neumann and Oskar Morgenstern’s Theory of Games and Economic Behavior. We argue that scholars with administrative leadership functions who were not only scientifically but also organizationally central – in our case the director of research Jacob Marschak – played a (...) crucial role in promoting the adoption of the Theory of Games. We support our argument with a scientometric analysis of all acknowledgments made in 488 papers published from 1944 to 1955 in the two main paper series at Cowles. We apply blockmodeling techniques to the acknowledgments network to reconstruct the formal and informal social structure at Cowles at the time. Our findings suggest that studies of the early adoption of scientific theories benefit from complementary perspectives on the role of academic leaders and scientists. Our study reveals the importance of formal and informal social structures and the agenda of administrative leaders in these structures to explain their adoption. (shrink)

Is capitalism inherently predatory? Must there be winners and losers? Is public interest outdated and free-riding rational? Is consumer choice the same as self-determination? Must bargainers abandon the no-harm principle? Prisoners of Reason recalls that classical liberal capitalism exalted the no-harm principle. Although imperfect and exclusionary, modern liberalism recognized individual human dignity alongside individuals' responsibility to respect others. Neoliberalism, by contrast, views life as ceaseless struggle. Agents vie for scarce resources in antagonistic competition in which every individual seeks dominance. This (...) political theory is codified in non-cooperative game theory; the neoliberal citizen and consumer is the strategic rational actor. Rational choice justifies ends irrespective of means. Money becomes the medium of all value. Solidarity and good will are invalidated. Relationships are conducted on a quid pro quo basis. However, agents can freely opt out of this cynical race to the bottom by embracing a more expansive range of coherent action. (shrink)

We consider how an epistemic network might self-assemble from the ritualization of the individual decisions of simple heterogeneous agents. In such evolved social networks, inquirers may be significantly more successful than they could be investigating nature on their own. The evolved network may also dramatically lower the epistemic risk faced by even the most talented inquirers. We consider networks that self-assemble in the context of both perfect and imperfect communication and compare the behaviour of inquirers in each. This provides a (...) step in bringing together two new and developing research programs, the theory of self-assembling games and the theory of network epistemology. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

Game theorists tend to model climate negotiations as a so-called ‘tragedy of the commons’. This is rather worrisome, since the conditions under which such commons problems have historically been solved are almost entirely absent in the case of international greenhouse gas emissions. In this paper, I will argue that the predictive accuracy of the tragedy model might not stem from the model’s inherent match with reality but rather from the model’s ability to make self-fulfilling predictions. I then sketch some (...) possible ways to dispel the tragedy, including (1) recognizing some ways the assumptions of the model fail, (2) taking seriously recent work suggesting that increasing greenhouse gas emissions is not in most nations’ own self-interest, and (3) preferring alternative models like collective risk dilemmas, bargaining games, or cooperative models. (shrink)

This paper argues, based on Lewis’ claim that communication is a coordination game (Lewis in Minnesota studies in the philosophy of science, University of Minnesota Press, Minneapolis, pp 3–35, 1975), that we can account for the communicative function of demonstratives without assuming that they semantically refer. The appeal of such a game theoretical version of the case for non-referentialism is that the communicative role of demonstratives can be accounted for without entering the cul de sac of trying to (...) construct conventions of ever-increasing complexity. Instead communication via demonstratives is explained with reference to the general, non-domain specific ability of human beings to solve games of coordination. Furthermore, there is empirical support for such a view. Judgments concerning demonstrative reference have been shown to be sensitive to judgments concerning common ground (Clark et al. in J Verb Learn Verb Behav 22:245–258, 1983), which is exactly what the non-referentialist account would predict. The game theoretical account also allows for an intuitively plausible, non-referentialist treatment of Speaks’ ‘trumping argument’ (Speaks in Philos Stud 174:709–734, 2017), as well as the Carnap/Agnew puzzle (Kaplan in Syntax Semant 9:221–43, 1970). (shrink)

I start with some famous comments by the philosopher (psychologist) Ludwig Wittgenstein because Pinker shares with most people (due to the default settings of our evolved innate psychology) certain prejudices about the functioning of the mind and because Wittgenstein offers unique and profound insights into the workings of language, thought and reality (which he viewed as more or less coextensive) not found anywhere else. The last quote is the only reference Pinker makes to Wittgenstein in this volume, which is most (...) unfortunate considering that he was one of the most brilliant and original analysts of language. -/- In the last chapter, using the famous metaphor of Plato’s cave, he beautifully summarizes the book with an overview of how the mind (language, thought, intentional psychology) –a product of blind selfishness, moderated only slightly by automated altruism for close relatives carrying copies of our genes--works automatically, but tries to end on an upbeat note by giving us hope that we can nevertheless employ its vast capabilities to cooperate and make the world a decent place to live. -/- Pinker is certainly aware of but says little about the fact that far more about our psychology is left out than included. Among windows into human nature that are left out or given minimal attention are math and geometry, music and sounds, images, events and causality, ontology (classes of things), dispositions (believing, thinking, judging, intending etc) and the rest of intentional psychology of action, neurotransmitters and entheogens, spiritual states (e.g, satori and enlightenment, brain stimulation and recording, brain damage and behavioral deficits and disorders, games and sports, decision theory (incl. game theory and behavioral economics), animal behavior (very little language but a billion years of shared genetics). Many books have been written about each of these areas of intentional psychology. The data in this book are descriptions, not explanations that show why our brains do it this way or how it is done. How do we know to use the sentences in their various way (i.e., know all their meanings)? This is evolutionary psychology that operates at a more basic level –the level where Wittgenstein is most active. And there is scant attention to context. -/- Nevertheless this is a classic work and with these cautions is still well worth reading. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019). (shrink)

I have read many recent discussions of the limits of computation and the universe as computer, hoping to find some comments on the amazing work of polymath physicist and decision theorist David Wolpert but have not found a single citation and so I present this very brief summary. Wolpert proved some stunning impossibility or incompleteness theorems (1992 to 2008-see arxiv dot org) on the limits to inference (computation) that are so general they are independent of the device doing the computation, (...) and even independent of the laws of physics, so they apply across computers, physics, and human behavior. They make use of Cantor's diagonalization, the liar paradox and worldlines to provide what may be the ultimate theorem in Turing Machine Theory, and seemingly provide insights into impossibility, incompleteness, the limits of computation, and the universe as computer, in all possible universes and all beings or mechanisms, generating, among other things, a non- quantum mechanical uncertainty principle and a proof of monotheism. There are obvious connections to the classic work of Chaitin, Solomonoff, Komolgarov and Wittgenstein and to the notion that no program (and thus no device) can generate a sequence (or device) with greater complexity than it possesses. One might say this body of work implies atheism since there cannot be any entity more complex than the physical universe and from the Wittgensteinian viewpoint, ‘more complex’ is meaningless (has no conditions of satisfaction, i.e., truth-maker or test). Even a ‘God’ (i.e., a ‘device’with limitless time/space and energy) cannot determine whether a given ‘number’ is ‘random’, nor find a certain way to show that a given ‘formula’, ‘theorem’ or ‘sentence’ or ‘device’ (all these being complex language games) is part of a particular ‘system’. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 2nd ed (2019) and Suicidal Utopian Delusions in the 21st Century 4th ed (2019) . (shrink)

Since Gintis is a senior economist and I have read some of his previous books with interest, I was expecting some more insights into behavior. Sadly he makes the dead hands of group selection and phenomenology into the centerpieces of his theories of behavior, and this largely invalidates the work. Worse, since he shows such bad judgement here, it calls into question all his previous work. The attempt to resurrect group selection by his friends at Harvard, Nowak and Wilson, a (...) few years ago was one of the major scandals in biology in the last decade, and I have recounted the sad story in my article ‘Altruism, Jesus and the End of the World—how the Templeton Foundation bought a Harvard Professorship and attacked Evolution, Rationality and Civilization -- A review of E.O. Wilson 'The Social Conquest of Earth' (2012) and Nowak and Highfield ‘SuperCooperators’ (2012).’ Unlike Nowak, Gintis does not seem to be motivated by religious fanaticism, but by the strong desire to generate an alternative to the grim realities of human nature, made easy by the (near universal) lack of understanding of basic human biology and blank slateism of behavioral scientists, other academics, and the general public. -/- Gintis rightly attacks (as he has many times before) economists, sociologists and other behavioral scientists for not having a coherent framework to describe behavior. Of course the framework needed to understand behavior is an evolutionary one. Unfortunately he fails to provide one himself (according to his many critics and I concur), and the attempt to graft the rotten corpse of group selection onto whatever economic and psychological theories he has generated in his decades of work, merely invalidates his entire project. Although Gintis makes a valiant effort to understand and explain the genetics, like Wilson and Nowak, he is far from an expert, and like them, the math just blinds him to the biological impossibilities and of course this is the norm in science. As Wittgenstein famously noted on the first page of Culture and Value “There is no religious denomination in which the misuse of metaphysical expressions has been responsible for so much sin as it has in mathematics.” -/- It has always been crystal clear that a gene that causes behavior which decreases its own frequency cannot persist, but this is the core of the notion of group selection. Furthermore, it has been well known and often demonstrated that group selection just reduces to inclusive fitness (kin selection), which, as Dawkins has noted, is just another name for evolution by natural selection. Like Wilson, Gintis has worked in this arena for about 50 years and still has not grasped it, but after the scandal broke, it took me only 3 days to find, read and understand the most relevant professional work, as detailed in my article. It is mind boggling to realize that Gintis and Wilson were unable to accomplish this in nearly half a century. -/- I discuss the errors of group selection and phenomenology that are the norm in academia as special cases of the near universal failure to understand human nature that are destroying America and the world. -/- Those who wish to read all my articles please consult the ebook here Philosophy, Human Nature and the Collapse of Civilization -- Articles and Reviews 2006-2016 by Michael Starks 662p (2016) -/- All of my papers and books have now been published in revised versions both in ebooks and in printed books. -/- Talking Monkeys: Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B071HVC7YP. -/- The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle--Articles and Reviews 2006-2016 (2017) https://www.amazon.com/dp/B071P1RP1B. -/- Suicidal Utopian Delusions in the 21st century: Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2017 (2017) https://www.amazon.com/dp/B0711R5LGX -/- . (shrink)

It is known that sociobiology, the theory of the biological origins of the social behavior of living beings, is related to ethics. However, sociobiology does not include moral doctrines but simply describes facts. The present essay discusses two basic theses, “altruism” and “reciprocal altruism”, in order to prove that a natural science free of judgments and evaluations is contrary to a theory of ethics, such as the theory of Kant and Apel, as well as to intuitive theories (...) of ethics. Ethics is the explanatory theory of morality, and morality should be established at the theoretical level, so that doubt can be expressed and not restrained. (shrink)

Behavior oftentimes allows for many possible interpretations in terms of mental states, such as goals, beliefs, desires, and intentions. Reasoning about the relation between behavior and mental states is therefore considered to be an effortful process. We argue that people use simple strategies to deal with high cognitive demands of mental state inference. To test this hypothesis, we developed a computational cognitive model, which was able to simulate previous empirical findings: In two-player games, people apply simple strategies at first. They (...) only start revising their strategies when these do not pay off. The model could simulate these findings by recursively attributing its own problem solving skills to the other player, thus increasing the complexity of its own inferences. The model was validated by means of a comparison with findings from a developmental study in which the children demonstrated similar strategic developments. (shrink)

How do people reason about their opponent in turn-taking games? Often, people do not make the decisions that game theory would prescribe. We present a logic that can play a key role in understanding how people make their decisions, by delineating all plausible reasoning strategies in a systematic manner. This in turn makes it possible to construct a corresponding set of computational models in a cognitive architecture. These models can be run and fitted to the participants’ data in (...) terms of decisions, response times, and answers to questions. We validate these claims on the basis of an earlier game-theoretic experiment about the turn-taking game “Marble Drop with Surprising Opponent”, in which the opponent often starts with a seemingly irrational move. We explore two ways of segregating the participants into reasonable “player types”. The first way is based on latent class analysis, which divides the players into three classes according to their first decisions in the game: Random players, Learners, and Expected players, who make decisions consistent with forward induction. The second way is based on participants’ answers to a question about their opponent, classified according to levels of theory of mind: zero-order, first-order and second-order. It turns out that increasing levels of decisions and theory of mind both correspond to increasing success as measured by monetary awards and increasing decision times. Next, we use the logical language to express different kinds of strategies that people apply when reasoning about their opponent and making decisions in turn-taking games, as well as the ‘reasoning types’ reflected in their behavior. Then, we translate the logical formulas into computational cognitive models in the PRIMs architecture. Finally, we run two of the resulting models, corresponding to the strategy of only being interested in one’s own payoff and to the myopic strategy, in which one can only look ahead to a limited number of nodes. It turns out that the participant data fit to the own-payoff strategy, not the myopic one. The article closes the circle from experiments via logic and cognitive modelling back to predictions about new experiments. (shrink)

Biologists and philosophers of biology have argued that learning rules that do not lead organisms to play evolutionarily stable strategies (ESSes) in games will not be stable and thus not evolutionarily successful. This claim, however, stands at odds with the fact that learning generalization---a behavior that cannot lead to ESSes when modeled in games---is observed throughout the animal kingdom. In this paper, I use learning generalization to illustrate how previous analyses of the evolution of learning have gone wrong. It has (...) been widely argued that the function of learning generalization is to allow for swift learning about novel stimuli. I show that in evolutionary game theoretic models learning generalization, despite leading to suboptimal behavior, can indeed speed learning. I further observe that previous analyses of the evolution of learning ignored the short term success of learning rules. If one drops this assumption, I argue, it can be shown that learning generalization will be expected to evolve in these models. I also use this analysis to show how ESS methodology can be misleading, and to reject previous justifications about ESS play derived from analyses of learning. (shrink)

The bi-polar confrontation between the Soviet Union and the USA involved many leading game theorists from both sides of the Iron Curtain: Oskar Morgenstern, John von Neumann, Michael Intriligator, John Nash, Thomas Schelling and Steven Brams from the United States and Nikolay Vorob’ev, Leon A. Petrosyan, Elena B. Yanovskaya and Olga N. Bondareva from the Soviet Union. The formalization of game theory (GT) took place prior to the Cold War but the geopolitical confrontation hastened and shaped its (...) evolution. In our article we outline four similarities and differences between Western GT and Soviet GT: 1) the Iron Curtain resulted in a lagged evolution of GT in the Soviet Union; 2) Soviet GT focused more on operations research and issues of centralized planning; 3) the contemporary Western view on Soviet GT was biased and Soviet contributions, including works on dynamic stability, non-emptiness of the core and many refinements, suggest that Soviet GT was able to catch up to the Western level relatively fast; 4) international conferences, including Vilnius, 1971, fostered interaction between Soviet game theorists and their Western colleagues. In general, we consider the Cold War to be a positive environment for GT in the West and in the Soviet Union. (shrink)

Certain apparently simple problems of coming to an agreement are surprisingly difficult to analyze in terms of individually rational behavior with a given set of preferences and beliefs. Though initially the solution appears obvious, the reasoning that would be needed to reach the solution on the part of a pair of rational individuals seems baroque and doubtful. This is used to suggest that a more fruitful tack is to analyze the situation in terms of a kind of joint or shared (...) intentionality. If that can be invoked, what appears simple in practice will turn out to be simple in theory as well. (shrink)

It is commonly thought that such topics as Impossibility, Incompleteness, Paraconsistency, Undecidability, Randomness, Computability, Paradox, Uncertainty and the Limits of Reason are disparate scientific physical or mathematical issues having little or nothing in common. I suggest that they are largely standard philosophical problems (i.e., language games) which were resolved by Wittgenstein over 80 years ago. -/- Wittgenstein also demonstrated the fatal error in regarding mathematics or language or our behavior in general as a unitary coherent logical ‘system,’ rather than as (...) a motley of pieces assembled by the random processes of natural selection. “Gödel shows us an unclarity in the concept of ‘mathematics’, which is indicated by the fact that mathematics is taken to be a system” and we can say (contra nearly everyone) that is all that Gödel and Chaitin show. Wittgenstein commented many times that ‘truth’ in math means axioms or the theorems derived from axioms, and ‘false’ means that one made a mistake in using the definitions, and this is utterly different from empirical matters where one applies a test. Wittgenstein often noted that to be acceptable as mathematics in the usual sense, it must be useable in other proofs and it must have real world applications, but neither is the case with Godel’s Incompleteness. Since it cannot be proved in a consistent system (here Peano Arithmetic but a much wider arena for Chaitin), it cannot be used in proofs and, unlike all the ‘rest’ of PA it cannot be used in the real world either. As Rodych notes “…Wittgenstein holds that a formal calculus is only a mathematical calculus (i.e., a mathematical language-game) if it has an extra- systemic application in a system of contingent propositions (e.g., in ordinary counting and measuring or in physics) …” Another way to say this is that one needs a warrant to apply our normal use of words like ‘proof’, ‘proposition’, ‘true’, ‘incomplete’, ‘number’, and ‘mathematics’ to a result in the tangle of games created with ‘numbers’ and ‘plus’ and ‘minus’ signs etc., and with -/- ‘Incompleteness’ this warrant is lacking. Rodych sums it up admirably. “On Wittgenstein’s account, there is no such thing as an incomplete mathematical calculus because ‘in mathematics, everything is algorithm [and syntax] and nothing is meaning [semantics]…” -/- I make some brief remarks which note the similarities of these ‘mathematical’ issues to economics, physics, game theory, and decision theory. -/- Those wishing further comments on philosophy and science from a Wittgensteinian two systems of thought viewpoint may consult my other writings -- Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle 2nd ed (2019), Suicide by Democracy 4th ed (2019), The Logical Structure of Human Behavior (2019), The Logical Structure of Consciousness (2019, Understanding the Connections between Science, Philosophy, Psychology, Religion, Politics, and Economics and Suicidal Utopian Delusions in the 21st Century 5th ed (2019), Remarks on Impossibility, Incompleteness, Paraconsistency, Undecidability, Randomness, Computability, Paradox, Uncertainty and the Limits of Reason in Chaitin, Wittgenstein, Hofstadter, Wolpert, Doria, da Costa, Godel, Searle, Rodych, Berto, Floyd, Moyal-Sharrock and Yanofsky (2019), and The Logical Structure of Philosophy, Psychology, Sociology, Anthropology, Religion, Politics, Economics, Literature and History (2019). (shrink)

Universal Game Theory - The theory that all of life is a game played by consciousness’es, (Living Beings). The board is a dream like structure of the universe. The progression is through an active process of intent witnessing, and passive meditation. Which releases the tension in the nerves of the body and leads to selfless actions, moral goodness, and eventually the finish, Enlightenment. Just like a wounded creature only cares about it’s own self. Man in tensionthrough (...) self-centered thought only thinks about his own predicaments. Even if he is thinking about others it is only through his own objectionable view from his false-created center. The enemy or deterrents in the game is the conditioning created by society for ambition, greed, control and general brain washing to give theindividual a false sense of identity, the ego. This is achieved by Religion; tradition, culture, education, reasoning, linear thought, and the creation of time. Leaving him/her in constant anxiety about millions of future contingencies. Through witnessing one realizes the whole futility of one dimensional thought which only leads to more and more thought. Simple or constructed elaborately, thought being time, a human construction. The final step of the Universal Game Theory is understanding the whole process of thought and the eventual revelation of Enlightenment, Bliss, ecstasy. Which the person then understands that he/she is the creator of the life/dream with which they are in. That point being a sexual ecstasy similar to what happens in death to men. i.e. Human males release semenwhen they die, having a sexual ecstasy at their last breath. (shrink)

Although the cognition is significant in strategic reasoning, its role has been weakly analyzed, because only the average intelligence is usually considered. For example, prisoner's dilemma in game theory, would have different outcomes for persons with different intelligence. I show how various levels of intelligence influence the quality of reasoning, decision, or the probability of psychosis. I explain my original methodology developed for my MA thesis in clinical psychology in 1998, and grant research in 1999, demonstrating the bias (...) of the classic IQ method, and how the intelligence limits thinking. Based on that I defined Personality Model, providing insight into understanding of psychosis (schizophrenia, bi-polar), which has not been explained yet by psychology or psychiatry. In addition, it enables to analyze and assess non-linear problems, utilizable in computer programming, visualization (animation) or other fields including Baduk game. I've already applied some principles in complex information system www.each.co.uk, and video-animations exhibited in London, Germany, Tokyo. I need to mention my experience in chess composition between 1994 and 2000, winning a few international prizes and inventing a special class of fairy rules redefining the mate. The chess composition principles or patterns show the way to organize logical series to higher advanced mechanisms (like calculus), applicable to other fields. One of such principles is a logical aesthetic innovation: new strategy, defined by Italian composers. Finally I show how the simple redefinition of the classic utility concept links economics and psychology to explain irrational / destructive behavior. All presented results (from the research) can be repeated. (shrink)

Game theory involves deliberating about what to do in light of what other people are likely to do. One of the central frameworks of game theory is the prisoner’s dilemma, in which participants who make rational choices end up in suboptimal outcomes. Using the prisoner’s dilemma to model competition between firms sets the stage for a new and promising approach to business ethics: the market failures approach.

One of the leading exponents of W's ideas on the language games of inner and outer (the `Two Selves' operation of our personality or intentionality or EP etc.) is the prolific Daniel Hutto (DH). His approach is called `Radical Enactivism' and is well explained in numerous recent books and papers (see my review of Radicalizing Enactivism) and a new one is appearing as I write (Evolving Enactivism). It is a development of or version of the Embodied Mind ideas now current (...) and, cleansed of its jargon, it is a straightforward extension of W's 2nd and 3rd period writings (though Hutto seems only intermittently aware of this). -/- Unfortunately, in 2006 Hutto had not yet arrived at his Radical Enactivism, so much time is wasted on McDowell and Brandom and of course none of them to this day have totally digested the later W and his prescient analysis of automatic behavior and the two systems of thought - so fully in tune with contemporary research. Nor is there any discussion of Searle's groundbreaking and completely Wittgensteinian (unwittingly) disquisitions on the Construction of Social Reality. Thus, his chapters 5 and 6 on Realism and Idealism etc., though superb for 2002, need a complete rewrite from a modern two systems viewpoint and I provide a start on that in my review. Much time is wasted on Davidson and Williams, etc. but one can endure them for Hutto's brilliant analyses and the frequent quotes from W. The last chapter gives his critic Rupert Read the counterblast he deserves and permits a slight update to 2006. Overall a lovely book and I eagerly await the third edition which I hope will ensue. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019) and Suicidal Utopian Delusions in the 21st Century 4th ed (2019). (shrink)

I start with some famous comments by the philosopher (psychologist) Ludwig Wittgenstein because Pinker shares with most people (due to the default settings of our evolved innate psychology) certain prejudices about the functioning of the mind, and because Wittgenstein offers unique and profound insights into the workings of language, thought and reality (which he viewed as more or less coextensive) not found anywhere else. There is only reference to Wittgenstein in this volume, which is most unfortunate considering that he was (...) the most brilliant and original analyst of language. -/- In the last chapter, using the famous metaphor of Plato’s cave, he beautifully summarizes the book with an overview of how the mind (language, thought, intentional psychology) –a product of blind selfishness, moderated only slightly by automated altruism for close relatives carrying copies of our genes (Inclusive Fitness)--works automatically, but tries to end on an upbeat note by giving us hope that we can nevertheless employ its vast capabilities to cooperate and make the world a decent place to live. -/- Pinker is certainly aware of but says little about the fact that far more about our psychology is left out than included. Among windows into human nature that are left out or given minimal attention are math and geometry, music and sounds, images, events and causality, ontology (classes of things or what we know), most of epistemology (how we know), dispositions (believing, thinking, judging, intending etc.) and the rest of intentional psychology of action, neurotransmitters and entheogens, spiritual states (e.g, satori and enlightenment, brain stimulation and recording, brain damage and behavioral deficits and disorders, games and sports, decision theory (incl. game theory and behavioral economics), animal behavior (very little language but a billion years of shared genetics). Many books have been written about each of these areas of intentional psychology. The data in this book are descriptions, not explanations that show why our brains do it this way or how it is done. How do we know to use the sentences in their various way (i.e., know all their meanings)? This is evolutionary psychology that operates at a more basic level –the level where Wittgenstein is most active. And there is scant attention to the context in which words are used = an arena which Wittgenstein pioneered. -/- Nevertheless, this is a classic work and with these cautions is still well worth reading. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019) . (shrink)

I give an informal presentation of the evolutionary game theoretic approach to the conventions that constitute linguistic meaning. The aim is to give a philosophical interpretation of the project, which accounts for the role of game theoretic mathematics in explaining linguistic phenomena. I articulate the main virtue of this sort of account, which is its psychological economy, and I point to the casual mechanisms that are the ground of the application of evolutionary game theory to linguistic (...) phenomena. Lastly, I consider the objection that the account cannot explain predication, logic, and compositionality. (shrink)

This is a two-volume set that provides an introduction to non-cooperative Game Theory. Volume 1 covers the basic concepts, while Volume 2 is devoted to advanced topics. The book is richly illustrated with approximately 400 figures. It is suitable for both self-study and as the basis for an undergraduate course in game theory as well as a first-year graduate-level class. It is written to be accessible to anybody with high-school level knowledge of mathematics. At the end (...) of each chapter there is a collection of exercises accompanied by detailed answers. The book contains approximately 180 exercises. (shrink)

I argue that the evolutionary history of anger has substantive implications for normative ethics. In the process, I develop an evolutionary account of anger and its influence on action. First, I consider a prominent argument by Peter Singer and Joshua Greene. They conclude that evolutionary explanations of human cooperation debunk – or undercut the evidential value of – the moral intuitions supporting duty ethics (as opposed to utilitarian or consequentialist ethics). With this argument they aim to defend consequentialist theories. However, (...) their argument also threatens to debunk intuitions that support consequentialist theories. I give a novel argument that overcomes this difficulty. Specifically, I offer an evolutionary story about anger that can explain retributive, duty-oriented intuitions concerning punishment. This explanation debunks these intuitions (and not others) by showing that they were selected for their biological consequences rather than their accuracy (concerning duties to punish). I develop this explanation by raising and resolving three additional problems. First, prominent evolutionary explanations of retributive motives fail because they appeal to models that apply only to organisms with strategic insight. To mitigate this problem, I explain the existence of a retributive-like motive in rodents by appealing to an economic model of resource competition, which applies to organisms without strategic insight. Second, I show that human anger was shaped by resource competition of this kind. To do so, I develop evidential criteria to determine when psychological systems in different species share common evolutionary origins. I deploy these criteria to argue that human anger and the retributive motive in rodents derive from the same ancestral trait. Finally, the continuity of anger across human and nonhuman animals stands in tension with the idea that anger causes purposive behavior like retribution or retaliation. I argue that differences between the angry behaviors of human and nonhuman animals are differences in degree and not in kind. In the end, this evolutionary story both explains and undermines retributive intuitions, but not in the straightforward way that Singer and Greene suppose. (shrink)

We apply spatialized game theory and multi-agent computational modeling as philosophical tools: (1) for assessing the primary social psychological hypothesis regarding prejudice reduction, and (2) for pursuing a deeper understanding of the basic mechanisms of prejudice reduction.

Modeling social interactions based on individual behavior has always been an area of interest, but prior literature generally presumes rational behavior. Thus, such models may miss out on capturing the effects of biases humans are susceptible to. This work presents a method to model egocentric bias, the real-life tendency to emphasize one's own opinion heavily when presented with multiple opinions. We use a symmetric distribution, centered at an agent's own opinion, as opposed to the Bounded Confidence (BC) model used in (...) prior work. We consider a game of iterated interactions where an agent cooperates based on its opinion about an opponent. Our model also includes the concept of domain-based self-doubt, which varies as the interaction succeeds or not. An increase in doubt makes an agent reduce its egocentricity in subsequent interactions, thus enabling the agent to learn reactively. The agent system is modeled with factions not having a single leader, to overcome some of the issues associated with leader-follower factions. We find that agents belonging to factions perform better than individual agents. We observe that an intermediate level of egocentricity helps the agent perform at its best, which concurs with conventional wisdom that neither overconfidence nor low self-esteem brings benefits. (shrink)

We argue that attempts to extrapolate moral motives for non-egoistic behavior in organizational behavior often interpret results empathically or deontically, while leaving other moral motivational frames, such as the utilitarian and virtue ethical, under-examined. We encourage the creation of experimental measures to distinguish various philosophical frames.

In recent work, Atran, Henrich, Norenzayan and colleagues developed an account of religion that reconciles insights from the ‘by-product’ accounts and the adaptive accounts. According to their synthesis, the process of cultural group selection driven by group competition has recruited our proclivity to adopt and spread religious beliefs and engage in religious practices to increase within group solidarity, harmony and cooperation. While their account has much merit, I believe it only tells us half the story of how institutional religions have (...) evolved. Their cultural evolutionary account of religion only looks at the cultural dynamics arising from competition between groups, not at the dynamics arising from within the group. Drawing from game-theoretic analyses of the emergence and cultural evolution of social institutions, I outline two sets of important ‘within-group’ dynamics that shape institutional religions. The first follow from the necessity to keep the interaction of the participants in an equilibrium state in order to maintain the social institution. The second arise from the competition of institutional features for traction within the group. Bringing these dynamics into account enables us to explain prominent features of institutional religions that cannot be satisfactorily explained by the current model of the cultural evolution of religions. (shrink)