Governance of social-ecological systems is a major policy problem of the contemporary era. Field studies of fisheries, forests, and pastoral and water resources have identified many variables that influence the outcomes of governance efforts. We introduce an experimental environment that involves spatial and temporal resource dynamics in order to capture these two critical variables identified in field research. Previous behavioral experiments of commons dilemmas have found that people are willing to engage in costly punishment, frequently generating increases in gross benefits, contrary to game-theoretical predictions based on a static pay-off function. Results in our experimental environment find that costly punishment is again used but lacks a gross positive effect on resource harvesting unless combined with communication. These findings illustrate the importance of careful generalization from the laboratory to the world of policy.

Abstract:Studies of collective action in commons dilemmas in social-ecological systems typically focus on scenarios in which actors all share symmetric (or similar) positions in relation to the common-pool resource. Many common social-ecological systems do not meet these criteria, most notably, irrigation systems. Participants in irrigation systems must solve two related collective action problems: 1) the provisioning of physical infrastructure necessary to utilize the resource (water), and 2) the asymmetric common-pool resource dilemma where the relative positions of head-enders and tail-enders generate asymmetric access to the resource itself (water). In times of scarcity, head-enders have an incentive to not share water with tail-enders. Likewise, tail-enders have an incentive to not provide labor to maintain the system if they do not receive water. These interdependent incentives may induce a cooperative outcome under favorable conditions. However, how robust is this system of interdependent incentives in the presence of environmental variability that generates uncertainty about water availability either through variation in the water supply itself or through shocks to infrastructure? This paper reports on results from laboratory experiments designed to address this question. Abstract Studies of collective action in commons dilemmas in social-ecological systems typically focus on scenarios in which actors all share symmetric (or similar) positions in relation to the common-pool resource. Many common social-ecological systems do not meet these criteria, most notably, irrigation systems. Participants in irrigation systems must solve two related collective action problems: 1) the provisioning of physical infrastructure necessary to utilize the resource (water), and 2) the asymmetric common-pool resource dilemma where the relative positions of "head-enders" and "tail-enders" generate asymmetric access to the resource itself (water). In times of scarcity, head-enders have an incentive to not share water with tail-enders. Likewise, tail-enders have an incentive to not provide labor to maintain the system if they do not receive water. These interdependent incentives may induce a cooperative outcome under favorable conditions. However, how robust is this system of interdependent incentives in the presence of environmental variability that generates uncertainty about water availability either through variation in the water supply itself or through shocks to infrastructure? This paper reports on results from laboratory experiments designed to address this question.

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ABSTRACT. Recently, there has been an increased interest in using behavioral experiments to study hypotheses on the governance of social-ecological systems. A diversity of software tools are used to implement such experiments. We evaluated various publicly available platforms that could be used in research and education on the governance of social-ecological systems. The aims of the various platforms are distinct, and this is noticeable in the differences in their user-friendliness and their adaptability to novel research questions. The more easily accessible platforms are useful for prototyping experiments and for educational purposes to illustrate theoretical concepts. To advance novel research aims, more elaborate programming experience is required to either implement an experiment from scratch or adjust existing experimental software. There is no ideal platform best suited for all possible use cases, but we have provided a menu of options and their associated trade-offs.

A major challenge in the development of computational models of collective behavior is the empirical validation. Experimental data from a spatially explicit dynamic commons dilemma experiment is used to empirically ground an agent-based model. Three distinct patterns are identified in the data. Two naïve models, random walk and greedy agents, do not produce data that match the patterns. A more comprehensive model is presented that explains how participants make movement and harvest decisions. Using pattern-oriented modeling the parameter space is explored to identify the parameter combinations that meet the three identified patterns. Less than 0.1% of the parameter combinations meet all the patterns. These parameter settings were used to successfully predict the patterns of a new set of experiments.

Being able to replicate research results is the hallmark of science. Replication of research findings using computational models should, in principle, be possible. In this manuscript, we assess code sharing and model documentation practices of 7500 publications about individual-based and agent-based models. The code availability increased over the years, up to 18% in 2018. Model documentation does not include all the elements that could improve the transparency of the models, such as mathematical equations, flow charts, and pseudocode. We find that articles with equations and flow charts being cited more among other model papers, probably because the model documentation is more transparent. The practices of code sharing improve slowly over time, partly due to the emergence of more public repositories and archives, and code availability requirements by journals and sponsors. However, a significant change in norms and habits need to happen before computational modeling becomes a reproducible science.

In traditional public good experiments participants receive an endowment from the experimenter that can be invested in a public good or kept in a private account. In this paper we present an experimental environment where participants can invest time during five days to contribute to a public good. Participants can make contributions to a linear public good by logging into a web application and performing virtual actions. We compared four treatments, with different group sizes and information of (relative) performance of other groups. We find that information feedback about performance of other groups has a small positive effect if we control for various attributes of the groups. Moreover, we find a significant effect of the contributions of others in the group in the previous day on the number of points earned in the current day. Our results confirm that people participate more when participants in their group participate more, and are influenced by information about the relative performance of other groups.

Software is data, but it is not just data. While "data" in computing and information science can refer to anything that can be processed by a computer, software is a special kind of data that can be a creative, executable tool that operates on data. However, software and data are similar in that they both traditionally have not been cited in publications. This paper discusses the differences between software and data in the context of citation, by providing examples and referring to evidence in the form of citations.