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

Two Approaches to CauseAgeFarm TypeSuccessorStatus

Adoption

Process

Prices

Opinions

Adoption

Opinions

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

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

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

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

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

Conclusions

• 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.