Resilient Cooperators Stabilize Long-Run Cooperation in the Finitely Repeated Prisoner's Dilemma

Mao, Andrew; Dworkin, Lili; Suri, Siddharth; Watts, Duncan J.

doi:10.2139/ssrn.2756249

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…Still, similar to our work, the authors use the experimental data to produce an algorithmic model and analyze its properties by simulations. Mao et al (2017) showed that different incentives and actions can be due to different understating of the world, or as side effect of not knowing the truth. Similar as in our world, where entities have only partial information (e.g.…”

Section: Related Workmentioning

confidence: 99%

Robust coordination in adversarial social networks: From human behavior to agent-based modeling

Hajaj

Joveski²,

Yu³

et al. 2021

Net Sci

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Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.

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Section: Related Workmentioning

confidence: 99%

Robust coordination in adversarial social networks: From human behavior to agent-based modeling

Hajaj

Joveski²,

Yu³

et al. 2021

Net Sci

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show abstract

“…For example, the platform explicitly separates experiment design and administration from implementation, promoting the development of reliable, replicable, and extendable research by enabling "experimentation-as-code." This modular structure encourages strategies such as multifactor (Almaatouq, Noriega-Campero, et al, 2020), adaptive (Letham, Karrer, Ottoni, & Bakshy, 2019;Balietti et al, 2020b;Paolacci et al, 2010;Balandat et al, 2020), and multiphase experimentation designs (Mao, Dworkin, Suri, & Watts, 2017;Almaatouq, Yin, & Watts, 2020), which dramatically expand the range of experimental conditions that can be studied. Additionally, the platform provides built-in data synchronization, concurrency control, and reactivity to natively support multi-participant experiments and support the investigation of macro-scale research questions.…”

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confidence: 99%

Empirica: a virtual lab for high-throughput macro-level experiments

et al. 2021

Self Cite

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Virtual labs allow researchers to design high-throughput and macro-level experiments that are not feasible in traditional in-person physical lab settings. Despite the increasing popularity of online research, researchers still face many technical and logistical barriers when designing and deploying virtual lab experiments. While several platforms exist to facilitate the development of virtual lab experiments, they typically present researchers with a stark trade-off between usability and functionality. We introduce Empirica: a modular virtual lab that offers a solution to the usability–functionality trade-off by employing a “flexible defaults” design strategy. This strategy enables us to maintain complete “build anything” flexibility while offering a development platform that is accessible to novice programmers. Empirica’s architecture is designed to allow for parameterizable experimental designs, reusable protocols, and rapid development. These features will increase the accessibility of virtual lab experiments, remove barriers to innovation in experiment design, and enable rapid progress in the understanding of human behavior.

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“…In economic theory, interactions between individuals in common-pool resource systems are commonly studied using game theory dilemmas, such as the well-known prisoner's dilemma (Kreps et al 1982, Mao et al 2017). These studies predict that under the assumption of economic rationality, individuals will over-exploit a resource to maximise their short-term personal profits.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis methodologies for analysing emergence using agent-based models

Molenaar

Ligtenberg²,

Voorn³

et al.

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Resilient Cooperators Stabilize Long-Run Cooperation in the Finitely Repeated Prisoner's Dilemma

Cited by 3 publications

References 54 publications

Robust coordination in adversarial social networks: From human behavior to agent-based modeling

Robust coordination in adversarial social networks: From human behavior to agent-based modeling

Empirica: a virtual lab for high-throughput macro-level experiments

Sensitivity analysis methodologies for analysing emergence using agent-based models

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