I challenge the usual approach of defining emergence in terms of properties of wholes “emerging” upon properties of parts. This approach indeed fails to meet the requirement of nontriviality, since it renders a bunch of ordinary properties emergent; however, by defining emergence as the incompressibility of a simulation process, we have an objective meaning of emergence because the difference between the processes satisfying the incompressibility criterion and the other processes does not depend on our cognitive abilities. Finally, this definition fulfills (...) the nontriviality and the scientific‐adequacy requirements better than the combinatorial approach, emergence here being a predicate of processes rather than of properties. †To contact the author, please write to: Institut d'Histoire et de Philosophie des Sciences et des Techniques (CNRS/Université Paris I Sorbonne), 13 rue du Four 75006, Paris; e‐mail: [email protected]. (shrink)

This article focuses on emergence in social systems. The author begins by proposing a new tool to explore the mechanisms of social emergence: multi agent–based computer simulation. He then draws on philosophy of mind to develop an account of social emergence that raises potential problems for the methodological individualism of both social mechanism and of multi agent simulation. He then draws on various complexity concepts to propose a set of criteria whereby one can determine whether a given social mechanism generates (...) emergent properties, in the sense that their explanation cannot be reduced to a mechanistic account of individuals and their interactions. This combined account helps to resolve the competing claims of methodological individualists and social realists. The author’s conclusion is that the scope of mechanistic explanation may be limited due to the extreme complexity of many social systems. Key Words: emergence • mechanism • computer simulation • methodological individualism • social realism. (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)

Understanding a normative concept like oppression requires attention to not only its harms but also the causes of those harms. In other words, a complete understanding of such a concept requires a proper causal explanation. This causal explanation can also inform and constrain our moral response to such harms. Therefore, the conceptual explanatory framework that we use to inform our moral diagnosis and our moral response become significant. The first goal of this dissertation is to propose complexity theory as the (...) proper framework for not only explaining a social phenomenon like oppression but also understanding the proper sites for social change. The second goal of this dissertation is to answer three interrelated questions about how we should respond, morally, to a chronic and complex social problem like racial or gender inequality: (1) Why do the current interventions to address these problems fail? (2) Do social movements play any unique role in addressing these problems? (3) What is our individual responsibility to participate in social movements? In response, I argue that the explanatory frameworks that we choose to understand the cause(s) of social problems can be the source of the inadequacy of our intervention. I argue that a proper social and moral intervention needs to capture the complex and dynamic nature of the social world. I also show that changing the explanatory framework allows us to see the unique role social movements play in making effective and sustainable social change possible. Finally, I conclude supporting such movements is a moral imperative. (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)

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)

Bedau's influential (1997) account analyzes weak emergence in terms of the non-derivability of a system’s macrostates from its microstates except by simulation. I offer an improved version of Bedau’s account of weak emergence in light of insights from information theory. Non-derivability alone does not guarantee that a system’s macrostates are weakly emergent. Rather, it is non-derivability plus the algorithmic compressibility of the system’s macrostates that makes them weakly emergent. I argue that the resulting information-theoretic picture provides a metaphysical account of (...) weak emergence rather than a merely epistemic one. (shrink)

In his book, History as a Science and the System of the Sciences, Thomas Seebohm articulates the view that history can serve to mediate between the sciences of explanation and the sciences of interpretation, that is, between the natural sciences and the human sciences. Among other things, Seebohm analyzes history from a phenomenological perspective to reveal the material foundations of the historical human sciences in the lifeworld. As a preliminary to his analyses, Seebohm examines the formal and material presuppositions of (...) phenomenological epistemology, as well as the emergence of the human sciences and the traditional distinctions and divisions that are made between the natural and the human sciences. -/- As part of this examination, Seebohm devotes a section to discussing Husserl’s formal mereology because he understands that a reflective analysis of the foundations of the historical sciences requires a reflective analysis of the objects of the historical sciences, that is, of concrete organic wholes (i.e., social groups) and of their parts. Seebohm concludes that Husserl’s mereological ontology needs to be altered with regard to the historical sciences because the relations between organic wholes and their parts are not summative relations. Seebohm’s conclusion is relevant for the issue of the reducibility of organic wholes such as social groups to their parts and for the issue of the reducibility of the historical sciences to the lower-order sciences, that is, to the sciences concerned with lower-order ontologies. -/- In this paper, I propose to extend Seebohm’s conclusion to the ontology of chemical wholes as object of quantum chemistry and to argue that Husserl’s formal mereology is descriptively inadequate for this regional ontology as well. This may seem surprising at first, since the objects studied by quantum chemists are not organic wholes. However, my discussion of atoms and molecules as they are understood in quantum chemistry will show that Husserl’s classical summative and extensional mereology does not accurately capture the relations between chemical wholes and their parts. This conclusion is relevant for the question of the reducibility of chemical wholes to their parts and of the reducibility of chemistry to physics, issues that have been of central importance within the philosophy of chemistry for the past several decades. (shrink)

Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes. Substantial leaps in computational force and software engineering methodologies now allow the design and development of large-scale biological models, which – when combined with advanced graphics tools – may produce realistic biological scenarios, that reveal new scientific explanations and knowledge about real life phenomena. A state-of-the-art simulation system termed Reactive Animation (RA) will serve as a study case to examine the contemporary philosophical debate (...) on the scientific value of simulations, as we demonstrate its ability to form a scientific explanation of natural phenomena and to generate new emergent behaviors, making possible a prediction or hypothesis about the equivalent real-life phenomena. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...) of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (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)

Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist us in (...) understanding emergent phenomena. (shrink)

Automatic preferences can influence a decision maker’s choice before any relevant or meaningful information is available. We account for this element of human cognition in a computational model of problem solving that involves active trial and error and show that automatic biases are not just a beneficial or detrimental property: they are a tool that, if properly managed over time, can give rise to superior performance. In particular, automatic preferences are beneficial early on and detrimental at later stages. What is (...) more, additional value can be generated by a timely rebiasing, i.e., a calculated reversal of the initial automatic preference. Remarkably, rebiasing can dominate not only debiasing but also continuously unbiased decision making. This research contributes to the debate on the adaptiveness of automatic and intuitive biases, which has centered primarily on one-shot controlled laboratory experiments, by simulating outcomes across extended time spans. We also illustrate the value of the novel intervention of adopting the opposite automatic preference—something organizations can readily achieve by changing key decision makers—as opposed to attempting to correct for or simply accepting the ubiquity of such biases. (shrink)

ArgumentAgent-based social simulations have historically been evaluated using two criteria: verification and validation. This article questions the adequacy of this dual evaluation scheme. It claims that the scheme does not conform to everyday practices of evaluation, and has, over time, fostered a theory-practice gap in the assessment of social simulations. This gap originates because the dual evaluation scheme, inherited from computer science and software engineering, on one hand, overemphasizes the technical and formal aspects of the implementation process and, on the (...) other hand, misrepresents the connection between the conceptual and the computational model. The mismatch between evaluation theory and practice, it is suggested, might be overcome if practitioners of agent-based social simulation adopt a single criterion evaluation scheme in which: i) the technical/formal issues of the implementation process are tackled as a matter of debugging or instrument calibration, and ii) the epistemological issues surrounding the connection between conceptual and computational models are addressed as a matter of validation. (shrink)

People's sense of being subject to digital dataveillance can cause them to restrict their digital communication behavior. Such a chilling effect is essentially a form of self-censorship in everyday digital media use with the attendant risks of undermining individual autonomy and well-being. This article combines the existing theoretical and limited empirical work on surveillance and chilling effects across fields with an analysis of novel data toward a research agenda. The institutional practice of dataveillance—the automated, continuous, and unspecific collection, retention, and (...) analysis of digital traces—affects individual behavior. A mechanism-based causal model based on the theory of planned behavior is proposed for the micro level: An individual's increased sense of dataveillance causes their subjective probability assigned to negative outcomes of digital communication behavior to increase and attitudes toward this communication to become less favorable, ultimately decreasing the intention to engage in it. In aggregate and triggered through successive salience shocks such as data scandals, dataveillance is accordingly hypothesized to lower the baseline of free digital communication in a society through the chilling effects mechanism. From the developed theoretical model, a set of methodological consequences and questions for future studies are derived. (shrink)

Among many properties distinguishing emergence, such as novelty, irreducibility and unpredictability, computational accounts of emergence in terms of computational incompressibility aim first at making sense of such unpredictability. Those accounts prove to be more objective than usual accounts in terms of levels of mereology, which often face objections of being too epistemic. The present paper defends computational accounts against some objections, and develops what such notions bring to the usual idea of unpredictability. I distinguish the objective unpredictability, compatible with determinism (...) and entailed by emergence, and various possibilities of predictability at emergent levels. This makes sense of practices common in complex systems studies that forge qualitative predictions on the basis of comparisons of simulations with multiple values of parameters. I consider robustness analysis as a way to ensure the ontological character of computational emergence. Finally, I focus on the property of novelty, as it is displayed by biological evolution, and ask whether computer simulations of evolution can produce the same kind of emergence as the open-ended evolution attested in Phanerozoic records. (shrink)

Policymakers increasingly draw on scientific methods, including simulation modeling, to justify their decisions. For these purposes, scientists and policymakers face an extensive choice of modeling strategies. Discussing the example of smallpox epidemiology, this paper distinguishes three types of strategies: Massive Simulation Models (MSMs), Abstract Simulation Models (ASMs) and Macro Equation Models (MEMs). By analyzing some of the main smallpox epidemic models proposed in the last 20 years, it discusses how to justify strategy choice with reference to the core characteristics of (...) these respective strategies. First, I argue that MEMs often suffice for policy purposes, and need to be replaced only if they are insufficiently robust. Such robustness results, however, only requires the use of ASMs, not MSMs. Second, I argue that although MSMs have larger potentials than ASMs in various dimensions, they are also more likely to fail—and that in many cases, this probability of failing outweighs their higher potential. In particular, these dimensions include the representation of the relevant target, the accurate measurement of the relevant parameters, the number of parameters included, the number of mechanisms modeled simultaneously, and the ways of dealing with structural uncertainty. While this in no way excludes the prospect that some MSMs provide good justifications for policy decisions, my arguments caution against a general preference for MSMs over ASMs for policy decision purposes in general and vaccination problems in particular. (shrink)

In the last 20 years network science has become an independent scientific field. We argue that by building network models network scientists are able to tame the vagueness of propositions about complex systems and networks, that is, to make these propositions precise. This makes it possible to study important vague properties such as modularity, near-decomposability, scale-freeness or being a small world. Using an epistemic model of network science, we systematically analyse the specific nature of network models and the logic behind (...) the taming mechanism. (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)

Constellations, Volume 29, Issue 2, Page 146-160, June 2022.

Recent philosophical discourse on emergence has developed with particular concern for the distinction between weak and strong emergence and with the primary focus on detailed analysis of the concept of supervenience. However, in the last decade and as a new departure, attention has been devoted to the distinction between synchronic and diachronic emergence. In this philosophical context, there is an ongoing general belief that these two concepts are so different that it is impossible to establish for them a general unifying (...) framework. It is the purpose of this paper to support an alternative view, i.e. that these concepts are different but not mutually exclusive, and that attending to appearance and persistence can, in this context, lead to an acceptable unifying framework for these two, differing concepts of emergence. (shrink)

I explore a type of computational social simulation known as artificial societies. Artificial society simulations are dynamic models of real-world social phenomena. I explore the role that these simulations play in social explanation, by situating these simulations within contemporary philosophical work on explanation and on models. Many contemporary philosophers have argued that models provide causal explanations in science, and that models are necessary mediators between theory and data. I argue that artificial society simulations provide causal mechanistic explanations. I conclude that (...) in their current form, these simulations are based on methodologically individualist assumptions that could limit their potential scope of social explanation. (shrink)

Cognitive Science, Volume 46, Issue 8, August 2022.

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 issue of whether formal reasoning or a computing-intensive approach is the most efficient manner to address scientific questions is the subject of some considerable debate and pertains not only to the nature of the phenomena and processes investigated by scientists, but also the nature of the equation and algorithm objects they use. Although algorithms and equations both rely on a common background of mathematical language and logic, they nevertheless possess some critical differences. They do not refer to the same (...) level of symbolization, as equations are based on integrated concepts in a denotational manner, while algorithms specifically break down a complex problem into more elementary operations, in an operational manner. They may therefore be considered as suited to the representation of different phenomena. Specifically, algorithms are by nature sufficient to represent weak emergent phenomena, but not strong emergent patterns, while equations can do both. Finally, the choice between equations and algorithms are by nature sufficient to represent weak emergent phenomena, but not strong emergent patterns, while equations behave conversely. We propose a simplified classification of scientific issues for which both equation- and/or algorithm-based approaches can be envisaged, and discuss their respective pros and cons. We further discuss the complementary and sometimes conflicting uses of equations and algorithms in a context of ecological theory of metapopulation dynamics. We finally propose both conceptual and practical guidelines for choosing between the alternative approaches. (shrink)

This article focuses on emergence in social systems. The author begins by proposing a new tool to explore the mechanisms of social emergence: multi agent–based computer simulation. He then draws on philosophy of mind to develop an account of social emergence that raises potential problems for the methodological individualism of both social mechanism and of multi agent simulation. He then draws on various complexity concepts to propose a set of criteria whereby one can determine whether a given social mechanism generates (...) emergent properties, in the sense that their explanation cannot be reduced to a mechanistic account of individuals and their interactions. This combined account helps to resolve the competing claims of methodological individualists and social realists. The author’s conclusion is that the scope of mechanistic explanation may be limited due to the extreme complexity of many social systems. (shrink)

The pervasive use of computer simulations in the sciences brings novel epistemological issues discussed in the philosophy of science literature since about a decade. Evolutionary biology strongly relies on such simulations, and in relation to it there exists a research program (Artificial Life) that mainly studies simulations themselves. This paper addresses the specificity of computer simulations in evolutionary biology, in the context (described in Sect. 1) of a set of questions about their scope as explanations, the nature of validation processes (...) and the relation between simulations and true experiments or mathematical models. After making distinctions, especially between a weak use where simulations test hypotheses about the world, and a strong use where they allow one to explore sets of evolutionary dynamics not necessarily extant in our world, I argue in Sect. 2 that (weak) simulations are likely to represent in virtue of the fact that they instantiate specific features of causal processes that may be isomorphic to features of some causal processes in the world, though the latter are always intertwined with a myriad of different processes and hence unlikely to be directly manipulated and studied. I therefore argue that these simulations are merely able to provide candidate explanations for real patterns. Section 3 ends up by placing strong and weak simulations in Levins’ triangle, that conceives of simulations as devices trying to fulfil one or two among three incompatible epistemic values (precision, realism, genericity). (shrink)

Bermúdez identifies the “Interface Problem” as the central problem in the philosophy of psychology: how commonsensical psychological explanations can be integrated with lower-level explanations? In particular, since folk psychology is meant to provide causal explanations on a par with, say, neurobiological explanations, the question of how to understand the relation between the two layers arises naturally. Donald Davidson claimed that the interface problem is actually ill-posed and put forward his version of the “Autonomy Picture”, the view known as anomalous monism. (...) This work reviews Davidson’s proposal in the light of digital universes: we model the key claims of the theory using cellular automata and show that Davidson’s original version of the Autonomy Picture is immune to two arguments against autonomy. (shrink)

The paper set up a small “philosophical lab” for thought experiments using Digital Universes as its main tool. Digital Universes allow us to examine how mereology affects the debate on New Realism of Ferraris and shed new light on the whole notion of Realism. The semi-formal framework provides a convenient way to model the varieties of realism that are important for the program of New Realism: we then draw the natural consequences of this approach into the ontology of our world, (...) arguing that the same considerations that apply to Digital Universe would hold for chess, institutions and social objects as well. Once a particular version of mereology is chosen, there are unavoidable consequences that the very underlying structure of social ontology. We then propose a new New Realism to tackle social objects: social objects turn out to be nothing more than mereological sums, picked up by some description. (shrink)

This paper analyzes three contrasting strategies for modeling intentional agency in contemporary analytic philosophy of mind and action, and draws parallels between them and similar strategies of scientific model-construction. Gricean modeling involves identifying primitive building blocks of intentional agency, and building up from such building blocks to prototypically agential behaviors. Analogical modeling is based on picking out an exemplary type of intentional agency, which is used as a model for other agential types. Theoretical modeling involves reasoning about intentional agency in (...) terms of some domain-general framework of lawlike regularities, which involves no detailed reference to particular building blocks or exemplars of intentional agency. Given the contrasting procedural approaches that they employ and the different types of knowledge that they embody, the three strategies are argued to provide mutually complementary perspectives on intentional agency. (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)

In managing the Covid-16 pandemic, policy makers took actions which require the cooperation of individual citizens to succeed while the actions partially come at remarkable costs for individuals. The brief paper employs a thought experiment to identify factors which affect individuals’ propensity to cooperate in the public goods game. These factors reasonably comprise, for example, risk perception and attitude towards risk, embeddedness in a social network or the desire for social approval and may differ remarkably among the individuals of a (...) collective. The paper adopts a management control perspective which appears to be particularly helpful to identify how to implement policy makers’ actions with respect to the diverse individuals in a collective. In order to predict the overall outcome of “unpleasant” actions, an approach is required which allows to capture the heterogeneity of individuals within a collective which makes agent-based modelling a promising candidate. (shrink)