Limitations and generalizability
The experimental approach to research patterns of expectation formation in the context of agricultural decision making and climate change delivers hints that these patterns are highly diverse among individuals engaged in the field of agriculture. Apart from differences in patterns of expectation formation rooted in the profession, specialization, personal experi-ences, age and worldviews of individuals in general, or with regard to the context of climate change related judgments in particular, trust in the experimental procedure and a tendency to show rational patterns of behavior might as well be influenced by a participant's famili-arity with abstract representations of a problem and his or her understanding of stochasticity
36 See Appendix section C3.1 for results of the statistical testing procedure.
37 See Appendix section C3.2 for results of the statistical testing procedure.
38 See Appendix section C3.3.1 and C3.3.2 for results of the statistical testing procedures.
and random realizations of outcomes, features that are related to the training an individual received.
Further, it is conceivable that the initial expectations stated by the participants might be influenced by (i) real world experience with current climate conditions, (ii) their actual belief on the direction of the effects of climate change in their region, or (iii) by an expec-tation on the intention of the institution or the experimenter conducting the research. In order to minimize these effects, a highly transparent experimental procedure was ensured.
Smith (1976) and Hey (1994) emphasize that in addition to this, with regard to the external validity of experimental approaches, it is essential to motivate participants with monetary payouts to ensure behavior during the experiment that corresponds to real-life behavior in economic decision contexts. Although statements on expectations about the climate in ef-fect were not directly rewarded, the motivation to earn money from crop production to be a sufficient incentive to make an informed and realistic guess on the actual climate can be expected, based on the analysis of consistency between stated climate expectations and risk level choices. On the contrary, rewarding correct guesses might have induced bias, as state-ments of indecisiveness would have been dis-incentivized.
While the random procedures involved in the experiment proved necessary in order to as-sure trust, and due to the ease of demonstrating independence compose one of the beauties of the experimental design, they also generate problems. The random climate selection pro-cedure as well as the determination of weather outcomes based on random draws pose problems for the experimenters: Resulting sequenced (and total monetary payments as well) are only predictable to a limited extent. As a result, several experimental sessions with sufficiently large groups of participants might be required to generate sufficient vari-ability in results and to establish generalizable results. Repeating treatments which have previously been randomly determined and communicating this in a trustful way to partici-pants has proven to be a remedy for the problem of too small sample sizes for one treatment.
Conclusions
A payout-motivated economic experiment was used to observe individual decision making under risk induced by climate change and to examine how expectations about altered cli-matic conditions are formed. To do so, weather outcomes during the experiment and de-pendent climate expectations of participants were observed. The experimental framework provides a way to “re-translate” statistically described information about the nature of cli-mate change into experiential information and the sequential nature of the decision task as well as the iterative character of learning induce a high degree of relatedness to the real-world characteristics of climate change. It thus incorporates decision structures and learn-ing trajectories and addresses mental processes that are highly relevant for decision maklearn-ing and taking actions, especially in the context of dealing with climate change outcomes. At the same time, the experimental design and implementation proved to be straightforward and easy to grasp as well for non-academics and people not used to working with comput-ers.
The findings underscore the importance of considering heterogeneity in expectation for-mation mechanisms, the rate of incorporation of inforfor-mation, and related individual-spe-cific valuation of climate and weather information. These are scientific questions of high relevance for policy makers, extension agents and communication experts concerned with climate change issues. Results were achieved using fictional probability distributions con-cerning the direction of climate change and were connected to hypothetical outcomes for crop profitability in the short to medium term. Provided the availability of probabilistic information on the direction of climate change in a certain region, the experimental design could be used to research behavior under predicted probability distributions for future cli-matic conditions and related changes in behavior could be measured.
For further theory development or even a quantitative estimate of the prevalence of differ-ent expectation formation patterns and relevant biases, the experimdiffer-ent will need to be re-peated on a larger scale, with more participants and a greater variety of weather-time series.
Estimates from these experiments can then be implemented in agent-based simulation mod-els to assess farmer climate adaptation and suitable policy interventions (Berger & Troost, 2014). In addition, more experiments will also allow for establishing robust statistical rela-tionships between demographic characteristics, learning-related variables, such as the indi-vidual-specific value of weather information, and their influence on expectation formation.
Ultimately, this could help clarifying whether increased training in dealing with statistical information could increase the degree of rationality involved with forming expectations on climatic change.
5 Discussion
The final chapter of this thesis discusses implications of the empirical findings for Agent-based modeling approaches applied to agricultural adaptation assessment and derives rec-ommendations on how the empirical findings could be used to inform more realistic repre-sentations of human decision making in the context of climate change response of the ag-ricultural sector. A last section refers to further research needs and recommendations for future research.