Accepting Government Payment for New Agri-Environmental Practices: A Simulation Approach to Social Complexity
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The document discusses methods for modeling the dynamics of farm decision-making based on social and economic factors, emphasizing the significance of social networks in the adoption process. It highlights a simulation model that portrays social interactions and decision thresholds among farmers, as well as the challenges of data collection related to these processes. The conclusions stress the need for a deeper understanding of motivations beyond simple variables and the importance of quantitative methods despite data gathering difficulties.
Accepting Government Payment for New Agri-Environmental Practices: A Simulation Approach to Social Complexity
- 1. 1 Project Background • Producing countryside not producing crops. • Novel approaches have social as well as economic responses. • Importance of social networks. • How to model inherently dynamic process?
- 2. 2 Two Approaches to Cause Age Farm Type Successor Status Adoption Process Prices Opinions Adoption Opinions
- 3. 3 How to Achieve This? • Qualitative methods to identify behavioural processes and inputs. • Simulation to represent process linking inputs and outputs. • Simulation - the representation of social processes as computer programs. • Introduction of dynamics.
- 4. 4 An Important Implication • The difference between social and economic variables is not simply a “label”, but may reflect the kinds of process by which these variables impact on the decision process. (Simple example: lexicographic preference.) • Advantage of simulator omniscience.
- 5. 5 A Simulation Model • Bias: Attitudes to measures. • Beliefs: Facts about impacts. • Farms differ in type. • Farmers pass messages through networks and use averaging. • Three decision-making thresholds. • “Actions speak louder than words.” • Role for farm advisors.
- 6. 6 Some Tentative Results •Variance in network size is very important. •De-adoption is a very bad advert for the measure. •Farm advisors an important source of motivation. •Relative weights of adopters and non-adopters very important.
- 7. 7 Problems with This Approach • Process data is actually very seldom collected. • It is very hard to collect. • Averaging leads rapidly to equilibrium. (Possible solution in non blending inheritance.)
- 8. 8 Conclusion s • Different kinds of motivations do not need to be represented simply as “variables”. • Simulation increases awareness of the importance of process. • Appropriate data nonetheless remains hard to gather, but quantitative methods become essential.