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Social preferences, learning, and the dynamics of cooperation in networked societies : = A dialogue between experimental and computational approaches.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Social preferences, learning, and the dynamics of cooperation in networked societies :/
Reminder of title:	A dialogue between experimental and computational approaches.
Author:	Cotla, Chenna Reddy.
Description:	1 online resource (234 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-01(E), Section: A.
Subject:	Economics. -
Online resource:	click for full text (PQDT)
ISBN:	9781339957487

Social preferences, learning, and the dynamics of cooperation in networked societies : = A dialogue between experimental and computational approaches.
Cotla, Chenna Reddy.

Social preferences, learning, and the dynamics of cooperation in networked societies :A dialogue between experimental and computational approaches. - 1 online resource (234 pages)

Source: Dissertation Abstracts International, Volume: 78-01(E), Section: A.

Thesis (Ph.D.)--George Mason University, 2016.

Includes bibliographical references

In this dissertation, I empirically investigate cooperative behavior in networks using the framework of network public goods games. To do so, I use a dialogue between behavioral experiments and agent-based models. I design and conduct behavioral experiments to generate data to construct boundedly rational agents that behave like humans and reproduce stylized facts in public goods environments. The human-like agents are deployed in a small-scale agent-based model to make novel quantitative predictions that can be statistically tested using a new set of behavioral experiments. This ensures that the behavioral specification of agents carries predictive value so that quantitative predictions made using it can be reproduced with human subject experiments. The high fidelity agent-based model is then extended to study the dynamics cooperation in networked environments. The dissertation is organized into three chapters.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339957487Subjects--Topical Terms:

555568
Economics.
Index Terms--Genre/Form:

554714
Electronic books.

Social preferences, learning, and the dynamics of cooperation in networked societies : = A dialogue between experimental and computational approaches.
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245 1 0 $a Social preferences, learning, and the dynamics of cooperation in networked societies : $b A dialogue between experimental and computational approaches.
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300 $a 1 online resource (234 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-01(E), Section: A.
500 $a Adviser: Robert L. Axtell.
502 $a Thesis (Ph.D.)--George Mason University, 2016.
504 $a Includes bibliographical references
520 $a In this dissertation, I empirically investigate cooperative behavior in networks using the framework of network public goods games. To do so, I use a dialogue between behavioral experiments and agent-based models. I design and conduct behavioral experiments to generate data to construct boundedly rational agents that behave like humans and reproduce stylized facts in public goods environments. The human-like agents are deployed in a small-scale agent-based model to make novel quantitative predictions that can be statistically tested using a new set of behavioral experiments. This ensures that the behavioral specification of agents carries predictive value so that quantitative predictions made using it can be reproduced with human subject experiments. The high fidelity agent-based model is then extended to study the dynamics cooperation in networked environments. The dissertation is organized into three chapters.
520 $a In the first chapter, using experimental data from a number of published studies on repeated public goods games, I examine the ex-post descriptive fit and ex-ante predictive accuracy of several learning models. I show that choices in repeated public goods games are best explained by an averaging reinforcement learning model. The second chapter builds on the experimental evidence that social preferences are necessary alongside with learning to explain contribution patterns in repeated public goods games. I design and conduct novel behavioral experiments to disentangle the roles of social preferences and learning in explaining repeated game choices. I find that choices in the repeated public goods games are best described by social preferences affecting the choice of first round contributions and then subsequent contributions based on payoff-based averaging reinforcement learning. I deploy the behavioral specification thus obtained in an agent-based model. Simulations using the agent-based model demonstrate a novel result that reducing the price of cooperation in the first round alone is sufficient to sustain significantly higher contributions over the later rounds of a repeated game. The quantitative predictions of the empirical agent-based model are successfully reproduced using follow-up behavioral experiments substantiating the predictive value of agents' behavioral specification.
520 $a In the third chapter, using the empirical agent-based model constructed from the experimental data, I show that network size, network density, degree heterogeneity, and the average path length of a network have no significant effect on the cooperation levels in public goods games. These results stand in contrast to the existing findings in the agent-based modeling literature and demonstrate that agent-based models based on empirical micro-foundations can lead to different conclusions than that of agent-based models based on micro-foundations extrapolated from other domains like that of biology. This dissertation illustrates that empirical understanding of determinants of behavior in social dilemmas is instrumental to identify mechanisms that can promote cooperation using agent-based models.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Economics. $3 555568
655 7 $a Electronic books. $2 local $3 554714
690 $a 0501
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a George Mason University. $b Computational Social Sciences. $3 1183791
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10140329 $z click for full text (PQDT)