Abstract
A number of philosophers (Rosenberg and Linquist in Anal Krit 27:136–157, 2005; Levy in J Philos 108(4):171–187, 2011; Arnold 2011, in Ethics Politics XV:101–138, 2013) have argued that agent-based, evolutionary game theory models of the evolution of cooperation fail to provide satisfying explanations of cooperation because they are too disconnected from actual biology. I show how these criticisms can be answered by employing modeling approaches from the situated cognition research program that allow for more biologically detailed models. Using cases drawn from recent situated cognition modeling research, I show how agent-based models of the evolution of cooperation can become more empirically-informed and relevant to explaining the evolution of cooperation in real populations. I argue that because situated cognition models allow for more detailed representations of not just agents’ brains but bodies and environments as well, they can be informed by a much wider breadth of empirical research than traditional agent-based models. Moreover, because the simulated bodies of these agents exhibit particular behaviors, they can be more readily compared to the behaviors of actual organisms. I conclude that employing situated cognition approaches to modeling the evolution of cooperation is a more promising way to investigate the evolution of cooperation than more traditional agent-based evolutionary game theory models.
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Notes
Arnold (2013) seems here to have confused a criticism made by Lazarus and Metcalfe (1990) and Masters and Waite (1990) for one made by Dugatkin because Dugatkin (1997) discusses it. However, Dugatkin attempts to dismiss the criticism by presenting empirical evidence that the shoaling tendency is not enough to explain observed behavior (1997, p. 68). Thus, it is worth pointing out that while Dugatkin (1997, 1998) does discuss and, at times, admit the seriousness of criticisms of game theoretic models of the evolution of cooperation, he ultimately advocates the modeling approach. Indeed, Dugatkin argues that while sometimes there is a disconnection between models and empirical research, the remedy must be the establishment of a feedback loop between models and empirical research (1998, pp. 57–58). But, given the continuation of criticisms of game-theoretic modeling approaches from philosophers (Rosenberg and Linquist 2005, Levy 2011) and scientists (Noë 2006; Akçay 2019) discussed subsequently in this section, it seems that the feedback loop remains insufficiently established.
Arnold here seems to be attributing the more cautious attitude of Dugatkin (1998) to Dugatkin (1997). Dugatkin (1997, p. 70) concludes from his survey that the prisoner’s dilemma is a good framework for studying cooperation in fish, while Dugatkin (1998, p. 57) ends by suggesting that the empirical challenges to the game-theoretic models described in the preceding paragraphs will require further adjustments to those models and new empirical tests.
Levy focuses on models in the evolution of morality, which has a strong overlap with research on the evolution of cooperation. Indeed, his case study is agent-based evolutionary game theory work by Skyrms on the origin of distributive justice. Just like Skyrms’s (2004) work on the evolution of cooperation using the stag hunt, his models of the evolution of fairness principles simulate agents playing a decision-making game about how to divide pre-existing goods (a ‘public goods’ game).
No particular ‘E’ (embodied, extended, enacted, embedded) is necessary to the situated cognition approach more generally. As Robbins and Aydede put it, “situated cognition is the genus, and embodied, enactive, embedded, and distributed [extended] cognition and their ilk are species” (2009, p. 3).
‘Where possible’ refers to the fact that while the traditional and situated models share certain parameters (like payoffs) that could be set to the same values, other parameters (such as parameters for connection strengths between neurons in the neural network) were only present in the situated model.
Such information is available. For example, see Speth (2010) for a detailed account of calories provided by big-game hunting as well as the nutrients that might be afforded by fat and protein from big-game sources, as well as the availability of such game. It is worth noting that Speth is skeptical of the nutritional/caloric value of big-game hunting playing a significant role in human evolution, but such information could nonetheless be used in the sort of modeling study I am describing to help support or challenge such skepticism.
Note that it is much easier for the situated cognition models I discuss here to make modifications that represent biological, rather than cultural, evolutionary processes. In this example, it is only possible to infer the social reward hypothesis from a failure of the caloric reward hypothesis to be borne out by the model. Situated cognition models thus seem to excel at capturing biological, neurological, and ecological detail, but are presently less apt at incorporating the social factors that would be necessary for simulating cultural evolutionary processes. Thus, in future research, I hope to show how situated cognition models can be modified to account for cultural evolutionary processes as well as biological ones.
It is worth pointing out that in order for situated cognition models to answer these sorts of fine-grained questions for particular systems, they may lose their applicability to providing more generalized explanations of the evolution of cooperation. After all, in order to answer such questions, models will need to be parameterized with data relevant to the specific case of interest and built to reflect the particular circumstances of those systems (e.g., representations of relevant features of agents’ brains, bodies, and environments). Thus, these models are likely to face the sort of trade offs of modeling desiderata (generality, precision, and realism) described first by Levins (1966) and more recently by Weisberg (2006). Broadly, these models sacrifice generality for precision and realism because the goal is to create models that more accurately represent their target systems than traditional game-theory models (creating realistic models) and providing model results that can be compared to relevant historical science data or behavioral data to provide support for the model (creating precise models). If the criticisms raised in section "Criticisms" are generally correct, then more traditional game-theoretic modeling efforts have sacrificed too much realism and/or precision to be applicable to answering these more fine-grained questions about how cooperation evolved in particular cases. Thanks to an anonymous reviewer for bringing the relevance of trade offs between model desiderata to my discussion of the advantages of situated cognition models to my attention.
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Acknowledgements
Thanks to my advisors Marc Ereshefsky and Megan Delehanty for many helpful rounds of feedback on this paper. Thanks also to Eckhart Arnold, Carl Brusse, Adrian Currie, and Jay Odenbaugh for their comments and suggestions on an earlier version of this paper. Finally, thanks to two anonymous reviewers whose comments also helped improve the paper.
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Fields, A. Better models of the evolution of cooperation through situated cognition. Biol Philos 36, 38 (2021). https://doi.org/10.1007/s10539-021-09813-2
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DOI: https://doi.org/10.1007/s10539-021-09813-2