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

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 web-based experiments, 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 theoretical 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 were contagious only to 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

Taking the Plunge: Online Data Collection Using MTurk

Mechanical Turk, an online crowdsourcing service created in 2005 by Amazon.com, is gaining in popularity among academics as a source of subjects for survey research. The initial attraction is based on access to a very large pool of participants who can be accessed online producing rapid response at low cost with minimal effort on the part of the researcher. For the uninitiated, however, the task of designing and running a research project on MTurk is rather daunting. This paper begins with an introduction to MTurk. We review the language of MTurk and explain how a researcher goes about setting up an online survey. Next we talke about several of the decisions that have to be made by the researcher. Decisions relate to the compensation rate to be paid, qualifications for respondents, timing decisions, and aspects of project design to aid in filtering out bad data. Such decisions are especially complicated when it comes to lengthy surveys

Assessing the Viability of Online Interruption Studies

Researchers have been collecting data online since the early days of the Internet and as technology improves, increasing numbers of traditional experiments are being run online. However, there are still questions about the kinds of experiments that work online, particularly over experiments with time-sensitive performance measures. We are interested in one time-sensitive measure specifically, the time taken to resume a task following an interruption. We ran participants through an archetypal interruption study online and in the lab. Statistical comparisons showed no significant differences in the time it took to resume following an interruption. However, there were issues with data quality that stemmed from participant confusion about the task. Our findings have implications for experiments that assess time-sensitive performance measures in tasks that require continuous attention

MTurk 101: An Introduction to Amazon Mechanical Turk for Extension Professionals

Amazon Mechanical Turk (MTurk) is an online marketplace for labor recruitment that has become a popular platform for data collection. In particular, MTurk can be a valuable tool for Extension professionals. As an example, MTurk workers can provide feedback, write reviews, or give input on a website design. In this article we discuss the many uses of MTurk for Extension professionals and provide best practices for its use

Detection of Abusive Language from Tweets in Social Networks

Detection of abusive language in user generated online con-tent has become an issue of increasing importance in recent years. Most current commercial methods make use of black-lists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted ex-samples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior

Collective intelligence: aggregation of information from neighbors in a guessing game

Complex systems show the capacity to aggregate information and to display coordinated activity. In the case of social systems the interaction of different individuals leads to the emergence of norms, trends in political positions, opinions, cultural traits, and even scientific progress. Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited. Here, we conducted an online experiment to investigate the performance of a collective when solving a guessing problem in which each actor is endowed with partial information and placed as the nodes of an interaction network. We measure the performance of the collective in terms of the temporal evolution of the accuracy, finding no statistical difference in the performance for two classes of networks, regular lattices and random networks. We also determine that a Bayesian description captures the behavior pattern the individuals follow in aggregating information from neighbors to make decisions. In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective. Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.Comment: 9 pages, 9 figure

Leaders should not be conformists in evolutionary social dilemmas

The most common assumption in evolutionary game theory is that players should adopt a strategy that warrants the highest payoff. However, recent studies indicate that the spatial selection for cooperation is enhanced if an appropriate fraction of the population chooses the most common rather than the most profitable strategy within the interaction range. Such conformity might be due to herding instincts or crowd behavior in humans and social animals. In a heterogeneous population where individuals differ in their degree, collective influence, or other traits, an unanswered question remains who should conform. Selecting conformists randomly is the simplest choice, but it is neither a realistic nor the optimal one. We show that, regardless of the source of heterogeneity and game parametrization, socially the most favorable outcomes emerge if the masses conform. On the other hand, forcing leaders to conform significantly hinders the constructive interplay between heterogeneity and coordination, leading to evolutionary outcomes that are worse still than if conformists were chosen randomly. We conclude that leaders must be able to create a following for network reciprocity to be optimally augmented by conformity. In the opposite case, when leaders are castrated and made to follow, the failure of coordination impairs the evolution of cooperation.Comment: 7 two-column pages, 4 figures; accepted for publication in Scientific Reports [related work available at arXiv:1412.4113

Emergence of communities and diversity in social networks

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.This work was supported by the National Nature Science Foundation of China under Grants 61573064, 71631002, 71401037, and 11301032; the Fundamental Research Funds for the Central Universities and Beijing Nova Programme; and the Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant). The Boston University work was supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE- 1213217, and by Defense Threat Reduction Agency Grant HDTRA1-14-1-0017, and Department of Energy Contract DE-AC07-05Id14517. (61573064 - National Nature Science Foundation of China; 71631002 - National Nature Science Foundation of China; 71401037 - National Nature Science Foundation of China; 11301032 - National Nature Science Foundation of China; Fundamental Research Funds for the Central Universities and Beijing Nova Programme; Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant); PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - Defense Threat Reduction Agency; DE-AC07-05Id14517 - Department of Energy)Published versio

Economic Games on the Internet: The Effect of $1 Stakes

Online labor markets such as Amazon Mechanical Turk (MTurk) offer an unprecedented opportunity to run economic game experiments quickly and inexpensively. Using Mturk, we recruited 756 subjects and examined their behavior in four canonical economic games, with two payoff conditions each: a stakes condition, in which subjects' earnings were based on the outcome of the game (maximum earnings of $1); and a no-stakes condition, in which subjects' earnings are unaffected by the outcome of the game. Our results demonstrate that economic game experiments run on MTurk are comparable to those run in laboratory settings, even when using very low stakes