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This study analyzes the role of cooperative behavior in facing the risk of encouraging innovative agricultural production projects by small-scale farmers in the context of farmers in developing countries.

A within-subjects field experiment was conducted with small-scale Colombian panela cane farmers. The authors used the collected data to run the regression analyses.

The results suggest that when small farmers can follow cooperative behavior by joining a group and pooling resources to face risk, they are more willing to invest in a novel and profitable alternative, albeit riskier. However, the possibility of cooperating with a group to invest in a novel production project depends on its expected risk level.

These results will help develop agricultural policies for sustainable development. Establishing informal networks for small-scale farmers to deal with unpredictable risks may aid in developing innovative systems.

Agriculture is highly vulnerable to climatic impacts, which, combined with the inherent risk of innovation, may reduce small farmers' willingness to adopt innovation. Cooperation appears to be a mechanism for pooling resources and facing risk.

Research has focused on experimentally testing the effect of cooperative behavior when facing risk. The authors contribute to the literature by demonstrating the impact of the ability of small-scale farmers in rural areas to collectively manage risk on investment in innovative projects.

The purpose of this paper is to consider queuing systems where captive repeat customers select a service facility each period. Are people in such a distributed system, with limited information diffusion, able to approach optimal system performance? How are queues formed? How do people decide which queue to join based on past experience? The authors explore these questions, investigating the effect of information availability, as well as the effect of heterogeneous facility sizes, at the macro (system) and micro (individual performance) levels.

Experimental economics, using a queuing experiment.

The authors find little behavioural difference at the aggregate level, but observe significant variations at the individual level. This leads the authors to the conclusion that it is not sufficient to evaluate system performance by observing average customer allocation and sojourn times at the different facilities; one also needs to consider the individuals’ performance to understand how well the chosen design works. The authors also observe that better information diffusion does not necessarily improve system performance.

Evaluating system performance based on aggregate behaviour can be misleading; however, this is how many systems are evaluated in practice, when only aggregate performance measures are available. This can lead to suboptimal system designs.

There has been little theoretical or empirical work on queuing systems with captive repeat customers. This study contributes to the understanding of decision making in such systems, using laboratory experiments based on the cellular automata approach, but with all agents replaced by humans.