Computational Emergence
ULS systems must satisfy the needs of participants at multiple levels of an organization. These participants will often behave opportunistically to meet their own objectives.
Some aspects of ULS systems will be “programmed” by properly incentivizing and constraining behavior rather than by explicitly prescribing. This research area explores the use of methods and tools based on economics and game theory (e.g., mechanism design) to ensure globally optimal ULS system behavior by exploiting the strategic self-interests of the system’s constituencies. This research area also includes exploring metaheuristics and digital evolution to augment the cognitive limits of human designers, so they can manage ongoing ULS system adaptation more effectively.
Relevant Glossary Terms
- ant-colony optimization
- autocatalysis
- autopoiesis
- Bayesian technique
- clear-box testing
- contract net
- crossover
- digital evolution
- digital software evolution
- fitness function
- glue
- game theory
- genetic algorithm
- genetic programming
- greedy algorithm
- hypermutation
- in situ control, reflection, and adaptation
- institution design
- mechanism design
- metaheuristics
- microeconomics
- mutator function
- NP-complete
- objective function
- particle swarm optimization
- platform
- refactor
- revelation principle
- satisfice
- simulated annealing
- speciation
- swarm intelligence
- test-driven design
- type-safe staged computation
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Recommended Reading
- Blum, C. & Roli, A. “Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison.” ACM Computing Surveys 35, 3 (September 2003): 268-308.
- Dorigo, M. & Stutzle, T. Ant Colony Optimization. Cambridge, MA: MIT Press, 2004.
- Koza, J. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.
- Koza, J. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA: MIT Press, 1994.
- Koza, J.; Bennett, F.; Andre, D.; & Keane, M. Genetic Programming III: Darwinian Invention and Problem Solving. San Francisco, CA: Morgan Kaufmann, 1999.
- Koza, J.; Keane, M.; Streeter, M.; Mydlowec, W.; Yu, J.; & Lanza, G. Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Norwell, MA: Kluwer Academic Publishers, 2003.
- Nisan, N. & Ronen, A. “Algorithmic Mechanism Design,” 129-140. Proceedings of the 31st ACM Symposium on Theory of Computing. Atlanta, GA, May 1–4, 1999.
- Sandholm, T. “Distributed Rational Decision Making,” 201–258. Multiagent Systems: A Modern Introduction to Distributed Artificial Intelligence. Weiss, G., ed. Cambridge, MA: MIT Press, 1999.
- Shneidman, J. & Parkes, D. “Rationality and Self-Interest in Peer to Peer Networks.” Proceedings of the 2nd International Workshop on Peer-to-Peer Systems (IPTPS ‘03). Berkeley, CA, Feb. 20–21, 2003. Berlin, Germany: Springer-Verlag, 2003.
- Thompson, A.; Layzell, P.; & Zebulum, R. “Explorations in Design Space: Unconventional Electronics Design Through Artificial Evolution.” IEEE Transactions of Evolutionary Computation 3, 3 (September 1999): 167–196.
- Thompson, A. “Notes on Design Through Artificial Evolution: Opportunities and Algorithms,” 17–26. Adaptive Computing in Design and Manufacture V. Berlin, Germany: Springer-Verlag, 2002.