Cooperation and Contagion in Web-Based, Networked Public Goods Experiments

By Siddharth Suri, Duncan J. Watts. SeCO Workshops, 2010

446 citations recorded by the source corpus.

A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of experiments on Amazon Mechanical Turk, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial "seed" players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation did not spread beyond direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs.

Subfields: Experimental and Cognitive Psychology, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7134126911c6a385e4089e0ee1859808a859eff3

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Citations in the corpus

Behavioral Experiments in Email Filter Evasion (2016, result): “While not ideal, there is now substantial evidence that results from the experiments using Amazon Mechanical Turk are often indistinguishable from those found in physical laboratories (Horton, Rand, and Zeckhauser 2011; Suri and Watts 2011).”
Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers (2013, unknown): “…features can further be combined with other unique attributes of the MTurk platform, such as the ability to pay variable rate bonuses and quickly assemble groups of workers (e.g., Suri & Watts, 2011) to produce experimental designs that are more elaborate than surveys or text-based experiments.”
Do short-term laboratory experiments provide valid descriptions of long-term economic interactions? A study of Cournot markets (2013, unknown): “Suri and Watts (2011) conducted different variations of public-good games arranged on one of five network topologies and find their results to be overall in line with a related study by Cassar (2007).”
An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results (2019, unknown): “…types of data (Behrend et al., 2011; Buhrmester et al., 2011; Casler et al., 2013; Eriksson & Simpson, 2010; Goodman, Cryder, & Cheema, 2013; Horton, Rand, & Zeckhauser, 2011; Kees et al., 2017; Mason & Watts, 2009; Paolacci & Chandler, 2014; Shapiro, Chandler, & Mueller, 2013; Suri & Watts, 2011).”
Conducting Clinical Research Using Crowdsourced Convenience Samples. (2016, background): “At any given time, many workers are completing HITs on MTurk, making it possible to set up and run remote interactive group tasks (e.g., Hawkins 2014, Mason & Watts 2012, Suri & Watts 2011).”
Validity and Mechanical Turk: An assessment of exclusion methods and interactive experiments (2017, methodology): “…Berinsky, Huber, & Lenz, 2012; Buhrmester, Kwang,& Gosling, 2011; Chandler, Mueller,& Paolacci, 2014; Goodman, Cryder, & Cheema, 2012; Horton, Rand, & Zeckhauser, 2011; Huff & Tingley, 2015; Leeper, 2016; Mason & Suri, 2012; Paolacci, Chandler, & Ipeirotis, 2010; Rand, 2012; Suri & Watts, 2011).”
An Analysis of Data Quality: Professional Panels, Student Subject Pools, and Amazon's Mechanical Turk (2017, result): “…Cryder, and Cheema 2013; Heen, Lieberman, and Miethe 2014; Hamby and Taylor 2016; Huff and Tingley 2015; Maynard 2014; Paolacci, Chandler, and Ipeirotis 2010; Peer et al. 2015; Simons and Chabris 2012; Sprouse 2011; Suri and Watts 2011), our study makes an important contribution to the literature.”
Games on graphs (2014, background): “Experiments involving static graphs [114], [94], [31], [34] show little evidence so far of a spatial benefit to cooperation.”
Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research (2013, methodology): “In addition, the service has been validated as a tool for conducting survey research [3,4], one-shot decision-making research [5,6], collective behavior experiments [7,8], for norming stimuli, and conducting behavioral linguistics experiments [9,10].”
Data collection in a flat world: the strengths and weaknesses of mechanical turk samples (2013, result): “…2011), validated the psychometric properties of MTurk responses (Buhrmester, Kwang, & Gosling, 2011), and replicated some of the classic findings in behavioral economics (Horton, Rand, & Zeckhauser, 2011; Suri & Watts, 2011) and decision-making research (Paolacci, Chandler, & Ipeirotis, 2010).”
Abusive Language Detection in Online User Content (2016, result): “A number of studies have shown that workers on produce results that are comparable to conducting laboratory studies [2, 7, 12, 17].”
Mindfulness, Work Climate, and Psychological Need Satisfaction in Employee Well-being (2015, background): “…(e.g., Buhrmester et al. 2011; Gosling et al. 2004), decision-making studies (e.g., Amir et al. 2012; Paolacci et al. 2010), behavior and cognitive behavior experiments (e.g., Jasmin and Casasanto 2012; Suri and Watts 2011), and research using clinical populations (e.g., Shapiro et al. 2013).”