Data from: Rational constraints and the evolution of fairness in the Ultimatum Game
Data files
Aug 05, 2015 version files 5.26 GB
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README_for_UG Sim - Non-monotonic without selection (N .pdf
196.84 KB
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UG Sim - Both roles initialized to rational behavior (N
166.65 MB
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UG Sim - Monotonic with selection (N
444.66 MB
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UG Sim - Monotonic with selection, high mutation rate (N
222 MB
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UG Sim - Monotonic with selection, low mutation rate (N
119.42 MB
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UG Sim - Monotonic with selection, very low mutation rate (N
44.32 MB
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UG Sim - Monotonic without selection (N
695.82 MB
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UG Sim - Non-monotonic with selection (N
254.19 MB
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UG Sim - Non-monotonic without selection (N
600.45 MB
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UG Sim - Non-monotonic, initialized with monotonic procedure (N
85.96 MB
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UG Sim - Proposers constrained to minimum non-zero offer (N
91.63 MB
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UG Sim - Responders constrained to accept only hyperfair offers (N
140.52 MB
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UG Sim - Responders initialized to accept all offers (N
178.22 MB
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UG Sim - Responders initialized to rational acceptance function (N
178.24 MB
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UG Sim - Responders initialized to reject all offers (N
132.75 MB
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UG Sim - Roles separate and heritable (N
176.71 MB
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UGS Data Legend.pdf
196.28 KB
Abstract
Behavior in the Ultimatum Game has been well-studied experimentally, and provides a marked contrast between the theoretical model of a self-interested economic agent and that of an actual human concerned with social norms such as fairness. How did such norms evolve, when punishing unfair behavior can be costly to the punishing agent? The work described here simulated a series of Ultimatum Games, in which populations of agents earned resources based on their preferences for proposing and accepting (or rejecting) offers of various sizes. Two different systems governing the acceptance or rejection of offers were implemented. Under one system, the probability that an agent accepted an offer of a given size was independent of the probabilities of accepting the other possible offers. Under the other system, a simple, ordinal constraint was placed on the acceptance probabilities such that a given offer was at least as likely to be accepted as a smaller offer. For simulations under either system, agents’ preferences and their corresponding behavior evolved over multiple generations. Populations without the ordinal constraint came to emulate maximizing economic agents, while populations with the constraint came to resemble the behavior of human players.