Modeling Human Decisions in Coupled Human and Natural Systems: Review of Agent-Based Models

1. Modeling Human Decisions in Coupled Human and Natural Systems: Review of Agent-Based Models Li An San Diego State University Mapping and Disentangling Human Decisions in Complex Human-Nature Systems AAAS Symposium, Washington, D.C. February 18, 2011

2. Coupled Human and Natural Systems(CHANS) Introduction Methods Results Conclusion Heterogeneity Nonlinearity and thresholds Feedback/adaptation Time legacy…

3. Agent-based Modeling • What it is? • Individual-based • Mimic real world processes • Agents & environment • Strengths of ABM: • Modeling individual decision making • Incorporating social/ecological processes, structure, norms, and institutional factors • Incorporating multi-scale and multi-disciplinary data • Mobilize the simulated world Introduction Methods Results Conclusion

4. Modeling Human Decisions Introduction Methods Results Conclusion • Many consequences • Economic • Environmental • Sustainability • Multi-disciplinary in nature • Psychology (e.g., cognitive maps) • Sociology (e.g., organization of agents) • Political sciences (e.g., game theory)… • Difficult to model at local levels

5. Objectives Introduction Methods Results Conclusion What methods have been used to model human decision-making and behavior? What are the potential strengths and caveats of these methods? What improvements can be made to better model human decisions in CHANS?

6. Review Methods Introduction Methods Results Conclusion • Paper search • Web of Science (key words attached in the paper) • Personal archives of agent-based modeling papers • Descriptive statistics • A total of 152 papers reviewed • Early models in 1994 • Exponential increase over time

7. Microeconomic Models • Rationality  bounded rationality • Effects of non-monetary variables: how to account for? • What function? Option 1 Option 2 > Option n > … > Option 1 Option 2… Take Option 2! Option n Introduction Methods Results Conclusion Humans make decisions to maximize revenues or returns ($) Humans optimize a certain utility-like functions

8. Space Theory-based Models • What relationships? Linear? • Weights of the space variables? Soil? Introduction Methods Results Conclusion Absolute space theory Relative space theory

9. Cognitive Models • Quantification of these abstract concepts • Psychological theories for building their relationships FoeClose + Evasion Fear + FoeFar - (Gras et al. 2009) Introduction Methods Results Conclusion Cognitive maps or abilities (e.g., memory, learning, innovation) Social norms, beliefs, perceptions, or intentions Reputation of other agents… Gras R, Devaurs D, Wozniak A, Aspinall A.. 2009. Artificial life 15:423-63.

10. Institution-based Models • Hard to code some institutions! Economic returns, utility, cognitive measures Introduction Methods Results Conclusion Closely linked to the above cognitive models Institution can explain why there are similarities across agents

11. Experience- or Preference-based Models • There could be theories that explain such experiences or preferences • Simple, straightforward, and self-evident; overuses make ABM less mechanistic Introduction Methods Results Conclusion • Effective real-world strategies • Can be articulated • Inductively derivable from observations • Variants: artificial intelligence, expert knowledge, and fuzzy logic…

12. Empirical- or Heuristic Models Introduction Methods Results Conclusion • No theories or other guidelines • Black- or grey-box data-driven approach (e.g., neural network or decision tree ) • Go through relatively complex data compiling, computation, and/or analysis. • Variants of this approach • Agent typology approach • Participatory modeling

13. Evolutionary Programming • A special type of empirical- or heuristic models • Computationally intensive • Consistent with findings from general econometric models Introduction Methods Results Conclusion • Computational processes similar to natural selection • Agents carry a series of numbers, characters, programs, or strategies (chromosomes) • Multiple parental strategies compete and evolve to produce offspring strategies (copying, cross-breeding, and mutation) higher fitness (intricate f(x) ) • Calculate approximated f(x) through fitting the data

14. Hypothetical and/or Calibration-based Models • Not all the possible candidates are available • Multiple rules or values, if subject to calibration, • could cancel out each other • Use it very cautiously! Introduction Methods Results Conclusion • No data or theories exist • Adopt hypothetical rules (likely based on common knowledge or experience) • Calibration: let the outcomes of the model decide what rules are good

15. Conclusions & Discussion Introduction Methods Results Conclusion • Not meant to be exclusive • Balance between simplicity and complexity when modeling human decisions in CHANS-related agent-based models • The KISS rule: “Keep it simple, stupid” (Axelrod 1997) • Develop mechanistic and/or process-based models (feedbacks, adaptation of decisions, and other complexities) • Develop protocols or architectures in modeling human decisions in CHANS: • By different types (e.g., agents, decisions, objectives…) • Hybrid • Advancements in other disciplines

16. Acknowledgements Questions? • Sarah Wandersee • Ninghua Wang • Alex Zvoleff • Gabriel Sady • National Science Foundation PIRE Program • Visit the Space-Time Analysis of Complex Systems (STACS) Group at http://complexity.sdsu.edu/ Lan@mail.sdsu.edu