Behaviour, uncertainty, and the role of information in resource management

This thesis is part of the extensive research on natural resource management under uncertainty. Natural resources and their exploitation by humans form a social-ecological system which is influenced by resource dynamics, the ecological system, resource users, and the formal and informal institutional governance systems. Specifically, I focus in this thesis on the role of information, uncertainty and behaviour in fisheries management. I analyse how various actors handle different kinds of uncertainty. I differentiate between social uncertainty (what do the other people in my community do?), strategic uncertainty (what is the best thing to do?), and lastly the inherent uncertainty of fish stocks (how to provide policy advice if resource dynamics are uncertain?). Another focus point is the role of information in fisheries management. I start with studying cooperative behaviour in informal institutions and how it is impacted by the framing of the problem. I continue with analysing how scientific information is created and used in the management process. Two of the chapters are analysed with experimental data gathered in a lab-in-the-field experiment conducted in 21 villages in rural Cambodia. Next to the experiments I also use survey data. The analysis in chapter 4 is based on a newly established data base of fish stock assessments and in chapter 5 a case study is used.

The thesis consists of six chapters. Chapter 1 provides the context in which this thesis is to be placed, motivates the research questions, and introduces the methods used. In chapter 2 and 3, the focus is on cooperation in informal institutions. Chapter 2 assesses the role of uncertainty and its impact on cooperation in natural resource management. I use a linear public good game and a threshold public good game to see what influences cooperative behaviour depending on the degree of uncertainty. In a linear public good game there is only social uncertainty (how much do other people cooperative to manage the resource), while in a threshold public good game there is also strategic uncertainty (the social optimal contribution strategy depends on the contributions of the other resource users). I find that individual preferences such as risk aversion and trust explain large parts of the cooperative behaviour while in the threshold public good game people use the threshold as a focal point, thus reducing the cooperative game to a coordination game.

Chapter 3 analyses the role of information and its interaction with uncertainty in cooperation. I frame the threshold public good game as either a public good or a public bad game and find that the success rate in reaching the threshold is higher in the public good framing than in the public bad framing. I also elicit beliefs of what people think the other resource users will contribute and find a framing effect here as well. People in the public bad treatment expect their peers to contribute a lot more than in the public good framing. Thus, in the public bad framing I observe that people overestimate the contributions of others which increases the risk of failing to reach the threshold.

While in chapter 2 and 3 focus on informal institutions, chapter 4 and 5 analyse parts of the informal institutional setting. While in the experimental settings the resource dynamic was fully known, this often differs in reality. Therefore, in chapter 4, the focus is on how uncertainty related to fish stocks is handled in management. Given the high uncertainty, fish stock assessment experts need to use intuitive judgement when estimating fish stock biomass. I test whether these judgement calls are influenced by behavioural factors such as anchoring. I find that the experts are influenced by the external pressure put on them as well as anchoring. Whenever there is uncertainty I find that experts anchor their estimates on the previous one, but without the room for judgement calls, the new estimates usually diverge from the old ones. This pattern is stronger if the fish stock is considered to be in a critical status. We find that experts handle the resource uncertainty by following a 'better safe than sorry' approach and provide overcautious estimates.

In chapter 5 the use of scientific information in the formal institutional setting is analysed. I use European Union fisheries management as an example for a formal institution. The European Common Fisheries Policy (CFP) is a science-based management approach. The EU employs a management system in which there is a clear separation between the creation of scientific information and its application to policy. I focus on how scientific information (e.g. fish stock assessments) is used in the management process and how well the EU is prepared to adjust to new information. I use the spatial distribution of fish stocks as an example to analyse the flexibility of the EU as an institution. Due to climate change the stocks are shifting towards the poles, however, the institutional system is too sticky to account for these shifts. A major hindrance in adjusting to distribution shifts is the distribution of national quotas. Thus, I argue that these obstacles could be overcome by establishing a predefined system that can redistribute national quotas. Having such a system would allow to make full use of the scientific information provided. The last chapter provides answers to the research questions, discusses their implications and suggests where future research should focus on.