Modeling Human Decisions in Coupled Human and

Natural Systems: Review of Agent-Based Models

Li AnSan Diego State University

Mapping and Disentangling Human Decisions in Complex Human-Nature Systems

AAAS Symposium, Washington, D.C. February 18, 2011

Coupled Human and Natural Systems(CHANS)

Introduction Methods Results Conclusion

Heterogeneity Nonlinearity and thresholds Feedback/adaptation Time legacy…

Agent-based Modeling

Introduction Methods Results Conclusion

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

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

Modeling Human Decisions

Introduction Methods Results Conclusion

1. What methods have been used to model

human decision-making and behavior?

2. What are the potential strengths and

caveats of these methods?

3. What improvements can be made to better

model human decisions in CHANS?

Objectives

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

Review Methods

Introduction Methods Results Conclusion

Humans make decisions to maximize revenues or returns ($)

Humans optimize a certain utility-like functions

Microeconomic Models

Introduction Methods Results Conclusion

Option 1

Option 2…

Option n

Option 2 > Option n > … > Option 1

Take Option 2!

• Rationality bounded rationality

• Effects of non-monetary variables: how to

account for?

• What function? 

Space Theory-based Models

Introduction Methods Results Conclusion

Absolute space theory

Relative space theory

Soil?

• What relationships? Linear?

• Weights of the space

variables?

Cognitive maps or abilities (e.g., memory, learning, innovation)

Social norms, beliefs, perceptions, or intentions

Reputation of other agents…

Cognitive Models

Introduction Methods Results Conclusion

Fear

FoeClose

FoeFar

+

-

Evasion+

Gras R, Devaurs D, Wozniak A, Aspinall A.. 2009. Artificial life 15:423-63.

• Quantification of these abstract concepts

• Psychological theories for building their relationships

(Gras et al. 2009)

Closely linked to the above cognitive models Institution can explain why there are

similarities across agents

Institution-based Models

Introduction Methods Results Conclusion

Economic returns, utility, cognitive measures

• Hard to code some institutions!

Effective real-world strategies Can be articulated Inductively derivable from observations Variants: artificial intelligence, expert

knowledge, and fuzzy logic…

Experience- or Preference-based Models

Introduction Methods Results Conclusion

• There could be theories that explain such experiences or preferences

• Simple, straightforward, and self-evident; overuses make ABM less mechanistic

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

Empirical- or Heuristic 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

Evolutionary Programming

Introduction Methods Results Conclusion

• A special type of empirical- or heuristic models • Computationally intensive• Consistent with findings from general

econometric models

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

Hypothetical and/or Calibration-based Models

Introduction Methods Results Conclusion

• Not all the possible candidates are available

• Multiple rules or values, if subject to calibration,

could cancel out each other

• Use it very cautiously!

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

Conclusions & Discussion

Introduction Methods Results Conclusion

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

Acknowledgements

Questions?