In this paper I argue that it is finally time to move beyond the Nagelian framework and to break new ground in thinking about epistemic reduction in biology. I will do so, not by simply repeating all the old objections that have been raised against Ernest Nagel’s classical model of theory reduction. Rather, I grant that a proponent of Nagel’s approach can handle several of these problems but that, nevertheless, Nagel’s general way of thinking about epistemic reduction in terms of (...) theories and their logical relations is entirely inadequate with respect to what is going on in actual biological research practice. (shrink)

The use of computer simulation for building theoretical models in social science is introduced. It is proposed that agent-based models have potential as a “third way” of carrying out social science, in addition to argumentation and formalisation. With computer simulations, in contrast to other methods, it is possible to formalise complex theories about processes, carry out experiments and observe the occurrence of emergence. Some suggestions are offered about techniques for building agent-based models and for debugging them. A scheme for structuring (...) a simulation program into agents, the environment and other parts for modifying and observing the agents is described. The article concludes with some references to modelling tools helpful for building computer simulations. (shrink)

The literature in agent-based social simulation suggests that a model is validated when it is shown to ‘successfully’, ‘adequately’ or ‘satisfactorily’ represent the target phenomenon. The notion of ‘successful’, ‘adequate’ or ‘satisfactory’ representation, however, is both underspecified and difficult to generalise, in part, because practitioners use a multiplicity of criteria to judge representation, some of which are not entirely dependent on the testing of a computational model during validation processes. This article argues that practitioners should address social epistemology to achieve (...) a deeper understanding of how warrants for belief in the adequacy of representation are produced. Two fundamental social processes for validation: interpretation and commensuration, are discussed to justify this claim. The analysis is advanced with a twofold aim. First, it shows that the conceptualisation of validation could greatly benefit from incorporating elements of social epistemology, for the criteria used to judge adequacy of representation are influenced by the social, cognitive and physical organisation of social simulation. Second, it evidences that standardisation tools such as protocols and frameworks fall short in accounting for key elements of social epistemology that affect different instances of validation processes. (shrink)

Even though the philosophy of simulation is intended as a comprehensive reflection about the practice of computer simulation in contemporary science, its output has been disproportionately shaped by research on equation-based simulation in the physical and climate sciences. Hence, the particularities of alternative practices of computer simulation in other scientific domains are not sufficiently accounted for in the current philosophy of simulation literature. This article centres on agent-based social simulation, a relatively established type of simulation in the social sciences, to (...) exemplify this claim. The analysis advanced has a twofold goal. First, it shows that the philosophy of agent-based social simulation, mostly developed by practitioners themselves, is, on one hand, heavily influenced by the methodological features of agent-based modelling and by the loose and fragmented character of social theory and, on the other hand, distinctively shaped by contrasting views of what it implies to do social research with virtual or artificial societies. Second, it suggests ways in which cross-fertilisation could enrich the philosophical understanding of computer simulation both in agent-based social simulation and in the philosophy of simulation. (shrink)

Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing scientific practice, but does so in several importantly different ways. Simulations in general, and computer simulations in particular, ought to be understood as techniques which, like many scientific techniques, can be employed in the service of various and diverse epistemic goals. We focus our attentions on the way in which simulations can function as (i) explanatory and (ii) predictive tools. We argue that a wide (...) variety of simulations, both computational and physical, are best conceived in terms of a set of common features: initial or input conditions, a mechanism or set of rules, and a set of results or output conditions. Studying simulations in these terms yields a new understanding of their character as well as a body of normative recommendations for the care and feeding of scientific simulations. (shrink)

The term “deep history” refers to historical accounts framed temporally not by the advent of a written record but by evolutionary events (Smail 2008; Shryock and Smail 2011). The presumption of deep history is that the events of today have a history that traces back beyond written history to events in the evolutionary past. For human kinship, though, even forming a history of kinship, let alone a deep history, remains problematic, given limited, relevant data (Trautman et al. 2011). With regard (...) to a deep history, one conjecture is that human kinship evolved from primate social systems in a gradual, more-or-less continuous manner (see Chapais 2008); another conjecture is that kinship, in accordance with the incest account of Claude Lévi-Strauss (1969) or the fanciful, tetradic account of Nicholas J. Allen (1986), “comes into existence with a leap” (Trautman et al. 2011: 176); and yet another, the account to be developed in this paper, is that kinship, as it is understood and lived by culture bearers today, is the consequence of a profound and qualitative evolutionary transformation going from an ancestral primate-like social system predicated on extensive face-to-face interaction to the relation-based social systems that characterize human societies (Read 2012). (shrink)

The energy domain is still dominated by equilibrium models that underestimate both the dangers and opportunities related to climate change. In reality, climate and energy systems contain tipping points, feedback loops, and exponential developments. This paper describes how to create realistic energy transition management models: quantitative models that can discover profitable pathways from fossil fuels to renewable energy. We review the literature regarding agent-based economics, disruptive innovation, and transition management and determine the following requirements. Actors must be detailed, heterogeneous, interacting, (...) learning, and strategizing. Technology should be represented as a detailed and heterogeneous portfolio that can develop in a bottom-up manner, using endogenous feedback loops. Assumptions about discount rates and the social cost of carbon should be configurable. The model should contain interactions between the global, national, local, and individual level. A review of modelling techniques shows that equilibrium models are unsuitable and that system dynamics and discrete event simulation are too limited. The agent-based approach is found to be uniquely suited for the complex adaptive sociotechnical systems that must be modelled. But the choice for agent-based models does not mean a rejection of other approaches because they can be accommodated within the agent-based framework. We conclude with practical guidelines. (shrink)

The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate (...) that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences. (shrink)

Whether collective agency is a coherent concept depends on the theory of agency that we choose to adopt. We argue that the enactive theory of agency developed by Barandiaran, Di Paolo and Rohde (2009) provides a principled way of grounding agency in biological organisms. However the importance of biological embodiment for the enactive approach might lead one to be skeptical as to whether artificial systems or collectives of individuals could instantiate genuine agency. To explore this issue we contrast the concept (...) of collective agency with multi-agent systems and multi-system agents, and argue that genuinely collective agents instantiate agency at both the collective level and at the level of the component parts. Developing the enactive model, we propose understanding agency – both at the level of the individual and of the collective – as spectra that are constituted by dimensions that vary across time. Finally, we consider whether collectives that are not merely metaphorically ‘agents’ but rather are genuinely agentive also instantiate subjectivity at the collective level. We propose that investigations using the perceptual crossing paradigm suggest that a shared lived perspective can indeed emerge but this should not be conflated with a collective first-person perspective, for which material integration in a living body may be required. (shrink)

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a (...) purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom‐up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding. (shrink)

Optimization is about finding the best available object with respect to an objective function. Mathematics and quantitative sciences have been highly successful in formulating problems as optimization problems, and constructing clever processes that find optimal objects from sets of objects. As computers have become readily available to most people, optimization and optimized processes play a very broad role in societies. It is not obvious, however, that the optimization processes that work for mathematics and abstract objects should be readily applied to (...) complex and open social systems. In this paper we set forth a framework to understand when optimization is limited, particularly for complex and open social systems. (shrink)

It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Despite that, many feel that such simulations somehow contribute to our understanding. This paper tries to clarify this intuition by investigating whether artificial societies provide potential explanations. It is shown that these potential explanations, if they contribute to our (...) understanding, considerably differ from potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum . The paper discusses how these two kinds explanatory strategies differ, and how potential functional explanations can be appraised. (shrink)

This paper contributes to the (re-)conceptualisation of responsible innovation by proposing an evolutionary economic approach that focuses on the role of consumers in the innovation process. After a discussion of the philosophical foundations and ethical implications of this approach, which bears an explanatory potential that has not been adequately considered in previous discussions of responsible innovation, we present a first step towards capturing the important but often neglected role of consumers in innovation processes (including responsible innovation): We propose an agent-based (...) model that incorporates a multidimensional space of characteristics in which new products or services are represented by more than the mere aspect of price and quality. Instead, innovations are denoted by a large set of characteristics, including also negative or harmful ones. The model is used to illustrate that consumers’ heterogeneity and bounded rationality – even if considered in a simple manner – indeed play a crucial role in the creation and diffusion of responsible innovation which can and should be used for further work in this field and for possible extensions of the model. (shrink)

Formidable difficulties face anyone trying to model social phenomena using a formal system, such as a computer program. The differences between formal systems and complex, multi-facetted and meaning-laden social systems are so fundamental that many will criticise any attempt to bridge this gap. Despite this, there are those who are so bullish about the project of social simulation that they appear to believe that simple computer models, that are also useful and reliable indicators of how aspects of society works, are (...) not only possible but within our grasp. This paper seeks to pour water on such optimism but, on the other hand, show that useful computational models might be ‘evolved’ In this way it is disagreeing with both naive positivist and relativistic post-modernist positions. However this will require a greater ‘selective pressure’ against models that are not grounded in evidence, ‘floating models’, and will result in a plethora of complex and context-specific models. (shrink)

Successful instances of interdisciplinary collaboration can eventually enter a process of disciplinarisation. This article analyses one of those instances: agent-based computational social science, an emerging disciplinary field articulated around the use of computational models to study social phenomena. The discussion centres on how, in knowledge transfer dynamics from traditional disciplinary areas, practitioners parsed several epistemic resources to produce new foundational disciplinary shared commitments, and how disagreements operated as a mechanism of differentiation in their production. Two parsing processes are examined to (...) illustrate this claim. The first one is the parsing of the qualitative–quantitative dualism, arguably the most important methodological disagreement in social science. The second one is the parsing of prediction, a key value in contemporary science. The analysis evidences that disagreements have fostered both external and internal dynamics of differentiation in agent-based computational social science. The former have permitted a more efficient use of epistemic resources, whereas the latter have forced practitioners to modify the foundational narrative and the agenda of the field. (shrink)

This paper describes a cross-cultural research project on the relation between how people conceptualize nature and how they act in it. Mental models of nature differ dramatically among and within populations living in the same area and engaged in more or less the same activities. This has novel implications for environmental decision making and management, including dealing with commons problems. Our research also offers a distinct perspective on models of culture, and a unified approach to the study of culture and (...) cognition. We argue that cultural transmission and formation does not consist primarily in shared rules or norms, but in complex distributions of causally-connected representations across minds in interaction with the environment. The cultural stability and diversity of these representations often derives from rich, biologically-prepared mental mechanisms that limit variation to readily transmissible psychological forms. This framework addresses a series of methodological issues, such as the limitations of conceiving culture to be a well-defined system or bounded entity, an independent variable, or an internalized component of minds. (shrink)

Computational sociology models social phenomena using the concepts of emergence and downward causation. However, the theoretical status of these concepts is ambiguous; they suppose too much ontology and are invoked by two opposed sociological interpretations of social reality: the individualistic and the holistic. This paper aims to clarify those concepts and argue in favour of their heuristic value for social simulation. It does so by proposing a link between the concept of emergence and Luhmann's theory of communication. For Luhmann, society (...) emerges from the bottom-up as communication and he describes the process by which society limits the possible selections of individuals as downward causation. It is argued that this theory is well positioned to overcome some epistemological drawbacks in computational sociology. (shrink)

This paper discusses partial results of an ongoing project focused on analysing the current usefulness and implications of developing research on agent-based social simulation models beyond academic, hobbyist or educational purposes. Design, development and testing phases of such modelling are discussed along with common issues evidence-driven modellers often face whilst collecting, analysing and modelling quantitative and qualitative data into social simulations. It also includes a discussion on the evidence gathered in published literature and structured interviews with researchers that have lead (...) mid- to long-term (3–5 years) projects on social simulation in Europe and the United States. Finally, good-practice recommendations are put forward by presenting a development methodology fully guided by evidence. (shrink)

A program for the simulation of rational social normative trust, predictive `trust,' and predictive reliance between agents will be introduced. It offers a tool for social scientists or a trust component for multi-agent simulations/multi-agent systems, which need to include trust between agents to guide the decisions about the course of action. It is based on an analysis of rational social normative trust (RSNTR) (revised version of M. Tuomela 2002), which is presented and briefly argued. For collective agents, belief conditions for (...) collective agency should be added. For the various forms of trust agents must have (at least) subjectively rational reasons to believe that the conditions of the trust account are fulfilled. A list of such reasons (of varied weights), e.g., given by empirical research, can manually be built into a parameter file or be generated by a calling program in a fixed format. From this list of reasons the program randomly generates a belief base for the agents of the artificial society. Reasons can be chained together so that one set of reasons satisfies several belief conditions. The program checks if the conditions are fulfilled for the artificial agents' social normative trust/predictive `trust'/`predictive reliance' in another agent that he will perform an action X. Each outcome is logged to a result file. In conclusion we discuss various aspects of the application of a trust component of the suggested kind in empirical research, social simulation, and multi-agent systems. (shrink)

While decision making scholarship in management has specifically addressed the objectivist assumptions within the rational choice model, a similar move within business ethics has only begun to occur. Business ethics scholarship remains primarily based on rational choice assumptions. In this article, we examine the managerial decision making literature in order to illustrate equivocality within the rational choice model. We identify four key assumptions in the decision making literature and illustrate how these assumptions affect decision making theory, research, and practice within (...) the purview of business ethics. Given the breadth of disciplines and approaches within management decision making scholarship, a content analysis of management decision making scholarship produces a greater range of assumptions with finer granularity than similar scholarship within business ethics. By identifying the core assumptions within decision making scholarship, we start a conversation about why, how, and to what effect we make assumptions about decision making in business ethics theory, research, and practice. Examining the range of possible assumptions underlying current scholarship will hopefully clarify the conversation and provide a platform for future business ethics research. (shrink)

Studies in economics and humanities generally have intrinsic problems that this work illustrates, along with innovations for overcoming them. The main limitations and weak-points of orthodox theory necessitate the use in their stead of other multi-disciplinary approaches, like complexity science, agent-based simulations and artificial life simulations. An example of an artificial life simulation applied in the economics field concerning the exchange process shows the benefits of such new conceptual and methodological instruments.

Logistics is gaining increased attention in companies since interconnectivity is increasing, and the interdependence among actors is enhanced due to challenges organizations are facing today . While the logistics discipline has been characterized by an efficiency focus based on positivistic assumptions, the challenges of today require a focus on effectiveness i.e. adaptive logistics based on extended assumptions. As firms are becoming more complex themselves in their relationships with suppliers and customers, and there is increased turbulence facing almost all industries, this (...) complexity needs to be taken into consideration by logistics researchers and practitioners in order to increase their understanding, and for the sense-making of logistics phenomena. The purpose of the thesis is to contribute to the further development of logistics research and practice by exploring, from a complexity perspective, how increased adaptation can enhance logistics effectiveness. In order to gain knowledge and understanding of how a complexity perspective could impact logistics, several methods have been used. Through extensive literature reviews from several disciplines, insights into what type of underlying assumptions dominates the discipline were gained. In order to understand logistics in its ?real? empirical setting, a topical study entitled ?real logistics? was performed, in which logistics practitioners were interviewed with a grounded theory-inspired approach. Case studies have also been performed, focusing on issues of effectiveness. Furthermore, for me to be able provide the logistics discipline with an approach which is ?proven? to be applicable and where ?operationalization? of the complexity perspective could be achieved, combined case and simulation studies have been performed. It is the firm conclusion that adaptive logistics is not a concept separated from human-mind and involvement, instead it is a transformative concept realized in the individual and collective sense-making processes guided by assumptions related to both filters. Hence, in order to move towards adaptive logistics it is the conclusion that this is a process involving reflections on assumptions and their impacts on logistics phenomena and processes, and operationalization of a new way of thinking through sense-making methods with close connection to the perceived contexts in question. For the reflective part, theoretical frameworks are needed, which are in line with assumptions similar to real-life experience by managers i.e. of a less mechanical character, with emphasis on the extended assumptions comprised in the complexity perspective. Secondly, in order to operationalize a different way of thinking and acting i.e. change mind-sets to embrace more complex considerations, and provide tangible results in a reasonable time period, it has been concluded that agent-based modeling provides a feasible and applicable method and tool. (shrink)