Abstract
There seems to be an overarching historical process in which life in small groups has evolved into life in large societies. This paper describes the design of a simulator for the study of that process. The simulator is named after David Hume (1711–1776), who presented a rich, informal, and still modern theory about the problems, useful inventions, and driving mechanisms in the evolution from small groups to large societies. HUME1.0 is a simulator that is meant to cover the interplay of some key factors and forces in that process. The focus is on division of labor, possible gains from specialization, risky exchange with more or less distant others, possible fraud, moral control, and (at the next stage) establishment of monitoring and punishing authorities. Finally, we discuss the status, purposes, and relevance of simulators like HUME1.0 that create well-defined artificial worlds and allow for some (thought) experimenting with them.
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Notes
Cf. [Plato(1997)], Protagoras, 320C ff.
Cooperation, again, broadly conceived. Evolution of Cooperation is the title of Axelrod’s book (1984) that made computer simulations a well-known research instrument for the study of social processes. The core of Axelrod’s approach were tournaments of strategies for iterated prisoner’s dilemmas. Despite all the criticisms that the approach received, and regardless of the fact that the approach is not a fully developed evolutionary approach, Axelrod, nevertheless, pioneered an evolutionary account of cooperation and other kinds of interactions of similar importance; cf. [Hegselmann (2009), 5679 ff.].
Hume had no idea about genetic mechanisms that explain how a kind of fellow feeling could evolve. In his An Enquiry concerning the Principles of Morals, published 1751, he explicitly stops explanatory questions at that point: “It is needless to push our researches so far as to ask, why we have humanity or a fellow-feeling with others. It is sufficient, that this is experienced to be a principle in human nature. We must stop somewhere in our examination of causes; and there are, in every science, some general principles, beyond which we cannot hope to find any principle more grounded” (Hume 1998, p. 109 fn).
In their book Bowles and Gintis focus on the integration of recent research. Hume is not discussed. However, a quotation of Hume heads a chapter. In that quotation Hume argues that for huge groups it is difficult to agree upon joint projects and to execute them; cf. Bowles and Gintis (2011, p. 79).
See Hardin (1982).
Of course, there exist almost risk-free exchange regimes, for instance via mediators. But that implies institutions and an already existing division of labor. In our pre-historical context such institutions not yet exist.
Cf. Hegselmann and Will (2010) for alternative strategic interaction structures. Note further that we often use a sort of monetary jargon. However, what we model is a prehistoric economy long before money was invented. Thus the model’s real “currency” is crop, prey, personal service, i.e., natural valuables of all sorts. To pay for something, baskets of valuables can be compiled.
In Hegselmann and Will (2010) and Will (2010) we describe two different structural scenarios. In the second scenario the agents have to decide whether to exchange within their group or to enter a central market. The scenario is inspired by a model criticized in Will and Hegselmann (2008). For a description and discussion of grid based modeling approaches cf. Hegselmann and Flache (1998) and Flache and Hegselmann (2001).
With S-agents that intend to exploit it may be different, depending upon the values of β, σ, and \(\varphi\). An exploiter may obtain his highest payoff by accepting a problem that does not correspond to his highest competence.
A more technical description of the matching procedure can be found in the appendix of Hegselmann and Will (2010).
Observing simulation runs one tends to ask: Are we “outliers”?
In a review of Bowles’s and Gintis’s book A Cooperative Species – Human Reciprocity and its Evolution (2011), Runciman appreciates that so many pieces of evidence are put together in a unifying story. But then he points out that in the evolutionary story a decisive step is missing: “… Bowles and Gintis have very little to say about what has happened during the ten or more millennia since our ancestors ceased to live in hunting and foraging bands and formed the large, permanently sedentary, agriculturally and in due course industrially productive and increasingly urbanized communities within which there evolved the armies, markets, churches, banks, law courts, schools, bureaucracies, parliaments and other formal institutions which are the staples of the sociological agenda” (Runciman 2012, p. 20). HUME1.0 can rightly be criticized for the same reasons – and HUME2.0 hopefully will not be. Cf. Runciman’s Stone Age Sociology (2005).
Typically the guiding questions are modified during the construction of a simulator as well.
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Acknowledgments
This study was partially supported by the project Emergence in the Loop (EmiL: IST-033841), funded by the Future and Emerging Technologies programme of the European Commission, in the framework of the initiative Simulating Emergent Properties in Complex Systems. This article draws on Hegselmann and Will (2010), where a technically more detailed description of the simulator design is given.
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Hegselmann, R., Will, O. From Small Groups to Large Societies: How to Construct a Simulator?. Biol Theory 8, 185–194 (2013). https://doi.org/10.1007/s13752-013-0110-6
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DOI: https://doi.org/10.1007/s13752-013-0110-6