This document summarizes an academic paper that presents a basic simulation model of information diffusion. The paper aims to synthesize relevant literature on innovation diffusion, complement existing farm decision models, and provoke suggestions for further development. It describes traditional diffusion curve models, micromodeling approaches, spatial models using cellular automata, and network models. The simulation defines agents with attributes like associates, location, adoption tendencies, and defines how messages are passed. Results show that factors like needing repeated messages, sparse social networks, change agents, and agent boredom significantly impact adoption rates, while other factors like initial adopters or positive/negative messages have little effect. Spatial correlation impacts the adoption pattern but not final levels.

1. 1
A Basic Simulation of
Information Diffusion
Edmund Chattoe and Nigel Gilbert
Centre for Research on Simulation in the
Social Sciences (CRESS)
http://www.soc.surrey.ac.uk/research/cress.html
Department of Sociology
University of Surrey
Guildford GU2 5XH
{E.Chattoe|N.Gilbert}@surrey.ac.uk

2. 2
Goals of the
Paper
• Synthesising relevant
innovation diffusion literature
• Complementing models of farm
decision
• Provoking suggestions for
further development

3. 3
Models of Innovation
Diffusion
• Traditional aggregrate curve
fitting with mathematical
functions
• Micromodeling
• Spatial models using Cellular
Automata
• Network models

4. 4
Stages in Innovation
Diffusion
• Knowledge
• Persuasion
• Decision
• Implementation
• Confirmation

5. 5
Definition of an
Agent
• Associates
• Location
• Readiness to adopt
• Willingness to believe
• Ability to benefit
• Nature of message passing
• Tendency to boredom

6. 6
Sample Window from
Simulation

7. 7
Diffusion with No Spatial
Correlation

8. 8
Diffusion with Spatial
Correlation

9. 9
Results: Looking for Non-
Linearity
• Needing to be told repeatedly
increases times rapidly
• Networks with few associates
per agent have very slow
adoption
• Change agents have a dramatic
effect on adoption rates

10. 10
Results Continued
• Boredom among agents reduces
diffusion dramatically
• Innovations which benefit few
agents slow diffusion
disproportionately

11. 11
Useful Negative Results
• Associates distribution makes
no difference
• Balance of positive/negative
word of mouth has little effect
• Number of initial adopters
makes little difference
• Spatial correlation affects
adoption pattern but not final
level of adoption