Human cooperation in changing groups in a large-scale public goods game.

How people cooperate to provide public goods is an important scientific question and relates to many societal problems. Previous research studied how people cooperate in stable groups in repeated or one-time-only encounters. However, most real-world public good problems occur in groups with a gradually changing composition due to old members leaving and new members arriving. How group changes are related to cooperation in public good provision is not well understood. To address this issue, we analyze a dataset from an online public goods game comprising approximately 1.5 million contribution decisions made by about 135 thousand players in about 11.3 thousand groups with about 234 thousand changes in group composition. We find that changes in group composition negatively relate to cooperation. Our results suggest that this is related to individuals contributing less in the role of newcomers than in the role of incumbents. During the process of moving from newcomer status to incumbent status, individuals cooperate more and more in line with incumbents.

Multiple method modelling reveals lack of robustness in natural resource management research.

Research on natural resource management like fisheries, irrigation systems or forestry traditionally uses case studies providing us with a rich, in-depth perspective on many single systems. This comes with a disadvantage - lacking comparability as differences between studies exist in variables examined, their operationalization or methods used. Thus, studies often disagree on important drivers for ecological success. However, due to design differences the reasons behind different results often remain unknown. One reason might be the impact of method choice. Hence, this article tests the influence of methods on model results. We use a high-quality data set, the Nepal Irrigation Institutions and Systems database (NIIS), developed at the Ostrom Workshop. It contains 263 cases, each record having information on around 600 variables. Multiple machine learning methods - random forests (RF), gradient boosting (GBM), shallow neural networks (SNN) and deep neural networks (DNN) - are compared with a standard statistical approach (multivariate linear regressions (MLR)). We try to answer the question whether these methods differ in estimating the relevance for success of such well-known concepts like participation of users, resource size, relations with other groups, and social capital among others. The results indicate that both agreements and substantial differences exist across methods which casts doubt on the robustness of previous results. Hence, we advise more caution in interpreting existing results. We see this research as a step towards increasing the robustness of results and improving both generalisability and reproducibility of natural resource management research.

Long-term evidence on cooperation and cultural differences in public goods dilemmas.

It is hard to isolate human cooperation features outside the prevalent experimental laboratory context in a controlled setting. Since cooperation rates are very context-sensitive, a new setting-a public goods game in an online game-is analysed. This unique dataset addresses multiple critical issues: it is more realistic, individuals have an intrinsic motivation to play, there are no observer effects, and data are available for more than 18 000 individuals playing over 10 months. Participants come from 10 countries, which allows us to test for cultural influences on cooperation. We find small differences in cooperation rates between countries, ranging from 8.5% (Argentina) to 14.1% (Greece). Moreover, cooperation remains stable over long periods of time. Different cultural, economic and religious backgrounds do not seem to have a discernible influence on cooperation rates. Instead, individual differences seem to play a larger role. Finally, cooperation levels may be lower than suggested by experiments from the classical laboratory context.

Distinguishing protest responses in contingent valuation: A conceptualization of motivations and attitudes behind them.

The percentage of protesters in contingent valuation surveys is substantial-about 20% across many studies. This paper seeks to clarify the motivations behind protest responses. In addition, the question whether the estimation of willingness to pay (WTP) is more biased by the exclusion or inclusion of protest bids is yet undecided. Methodological improvements are difficult for three reasons: motivations behind protest responses are largely unclear, definitions of protest differ between studies and often only participants who state a zero WTP are asked for their reasons. Our survey on farm animal welfare (n = 1335) provides detailed motivations, two definitions and includes debriefing of all participants for their WTP. We find that protest bids are not a refusal to answer, they are neither irrational nor driven by lack of understanding. Quite the contrary, a large part of participants is directly motivated by moral reasons. Furthermore, protest responses are not coupled to a zero WTP. In our sample, only 8% out of 32% protesting participants had a zero WTP. Only a small fraction of zero bids (0.4%) are true WTP-statements, i.e. respondents were satisfied with the status quo. This finding has important implications for existing WTP-estimates which might be biased. Finally, we provide detailed estimates of the WTP for animal welfare issues by including and excluding different types of protesters and outliers.

Willingness to pay and moral stance: The case of farm animal welfare in Germany.

This study examines the impact of ethical attitude on the willingness to pay for farm animal welfare improvement in Germany. Little is known about the pluralism of moral attitudes that may exist behind farm animal welfare issues and its relationship to customers' willingness to pay for it. Via a large survey (n = 1334) we are able to identify different moral dimensions by employing validated scales. We find utilitarian alongside deontological attitudes as well as a mixture of both. Thus, presupposing a standard moral attitude is too simple. This has implications for decision-making on markets, since the implicit normative assumptions of a utilitarian position in economics has to be critically assessed. Furthermore, we asked for the willingness to pay for various aspects of farm animal welfare improvement. We find significant positive correlations between willingness to pay and environmental concern, altruism and less apathy. Measured in Euro, a higher environmental concern has the strongest effect on WTP for all five moral scales. Outliers with higher bids are willing to pay almost five times for any aspect of farm animal welfare than the rest of the sample. A more detailed analysis of outliers demonstrates that market-based approaches have restrictions in capturing certain moral values. Moreover, the motivations behind zero bids reveal that moral concerns outweigh indifference towards animal welfare by far. This has implications how policy can be designed to serve people's demand for higher animal welfare standards. Two other findings are of interest. First, we find a very high number of people assigning an intrinsic value to animals (90%). Second, zero bids and outlier treatment in WTP-studies deserves more careful consideration, since WTP-estimates are easily skewed by excluding these groups.