1. Agent-based Systems in geosimulation Geog 220, Winter 2005 Arika Ligmann-Zielinska February 14, 2005

2. Sources 1) Weiss G. ed. (1999) Multiagent Systems: a modern approach to distributed artificial intelligence, Cambridge, MA, MIT Press Prologue pp. 1 – 9 Chapter 1 Intelligent Agents by Michael Wooldridge pp. 27 – 42 Chapter 2 Multiagent Systems and Societies of Agents by Michael N. Huhns and Larry M. Stephens pp. 79 – 84 2) Batty M., Jiang B. (1999) Multi-agent Simulation: new approaches to exploring space-time dynamics within GIS, CASA paper 10 pp. 1 – 7 3) Benenson I., Torrens P. (2004) Geosimulation Automata- based Modeling of Urban Phenomena, John Wiley & Sons, LTD Chapter 6 Modeling Urban Dynamics with Multiagent Systems pp.154 – 184

3. Outline Agency Distributed Artificial Intelligence & Multi Agent Systems Agents environments Agents in geosimulation General typology of agents & urban agents Location choice behavior General Models of Urban Agents Examples

4. Agents Demystified agere (Latin) – to do Agent - a computational entity such as a software program or robot that can be viewed as perceiving and acting upon its environment and that is autonomous in that its behavior at least partially depends on its own experience Agent - system that decides for itself what it needs to do in order to satisfy its objectives Characteristics Autonomous Goal-oriented Interacting – agents “sense” or are “aware” of other agents Key behavioral processes Problem solving Planning Decision-making Learning When and how to interact with whom?

5. Agents Demystified Intelligent agents - agents operating robustly in rapidly changing, unpredictable, or open environments “Sense the future” Flexible autonomous action in order to meet design objectives (flexibility – reactivity) Pro-activeness (goal directed behavior, taking the initiative) Social ability (interact with other agents/humans) Effective integrating goal-oriented and reactive behavior

6. Multiagent Systems (MAS) MAS – a community of agents, situated in an environment. MAS – systems in which several interacting, intelligent agents pursue some set of goals or perform some set of tasks. –Inherent distribution (spatial, temporal, semantic, functional) –Inherent complexity MAS studied by Distributed Artificial Intelligence – DAI DAI and AI –AI – intelligent BUT stand-alone systems Intelligence acts in isolation Cognitive processes of individuals Psychology and behaviorism –DAI – intelligent connected systems Intelligence acts through interaction Social processes in groups of individuals Sociology and economics Hence DAI is a generalization of AI, and not its specialization!

7. Agents’ environment Accessible vs. inaccessible Deterministic vs. non-deterministic Episodic vs. non-episodic Static vs. dynamic Discrete vs. continuous What typology can be assigned to urban/spatial models? If an environment is sufficiently complex, the fact that it is actually deterministic is not much help – Why?

8. Summary of MAS attributes

9. Why Agents in Spatial Models? Urban systems are a product of human decisions CA cousins lack –Mobility –Purposefulness –Social ability –Adaptability –Transition Rules heterogeneity Refer to Figure 5.4 p. 169 in BenTor

10. Types of Agents Geosimulation: mobile, adaptive &…? Weak vs. strong agency Geosimulation deals with weak agents

11. Urban Agents Characteristic time ”t” years 10 th of seconds seconds month s

12. Urban Agent Choice Behavior Location and migration behavior Changes in state and location Mobile agents carry their characteristics with them Ability to make decision concerning the entire urban space (action-at-a-distance) Location choice modeled with rational decision-making and bounded rationality Utility Functions Set of opportunities {C i }available for agent A, where each C i has some level of Utility U(A, C i ) and/or Disutility D(A, C i ) = 1 – U(A, C i ) (assumed that U belongs to [0,1]) Variability in the perception of utility – choice probabilities P(A, C i ), where P(A, C i ) = f(U(A, C i )) e.g. logit model

13. Bounded Rationality Heuristics Random choice: pick one of the opportunities C i randomly Satisfier choice: pick one of the opportunities C i randomly and compare it to a pre-defined threshold Th A of an Agent A if U(A, C i ) > Th A pick C i Ordered choice: order C i for A in descending order, creating an ordered set of opportunities, pick the first opportunity from this set

14. Residential Decision Making Experimental results based on: Revealed preferences of subjects Stated preferences of subjects Taxonomy of residential decision-factors (adapted from Speare, 1974): Individual Household Housing Neighborhood Above-neighborhood Stress(dissatisfaction/dissonance)-resistance Residential Behavior (steps): Decision to leave the current location Decision to reside in a new location

15. General Models of Agents’ Collectives Diffusion-Limited Aggregation (DLA) Urban context – simulating new building locations: DLA of Developers Efforts Monocentricity (CBD core) Sprawl diffusion Urban land use density represented by power law: Density(d) ~ d D-2 d – distance from the city center D – fractal dimension Nicholas Gessler UCLA http://www.sscnet.ucla.edu/geog/gessler/borland/ http://www.sscnet.ucla.edu/geog/gessler/borland/

16. General Models of Agents’ Collectives Percolation Percolation of the Developers’ Efforts Developers build close to existing constructions Clustered Multicenteric Density of urban uses decreases according to exponential law Density(d) = d 0 e -Ld d – distance from the city center L – constant Real Simulation Image source: http://lisgi1.engr.ccny.cuny.edu/~makse/urban.html

17. General Models of Agents’ Collectives Intermittency Bifurcation of a cell Each time a fraction α of population leaves a cell C α distributes among von Neumann neighborhood of C – close migration C becomes an attractor or repelling center – distant migration Exponential decrease in density of urbanized land from the city center

18. General Models of Agents’ Collectives Spatiodemographic processes Particles are born and die Parameters of reproduction β and mortality γ –γ T T - threshold –Partially clustered Diffusion of Innovation probability of acceptance 1 – γ γ T (T – threshold) defined as intensity of innovation dissemination β

19. ABM in Urban Context - Examples XJ Technologies demos http://www.xjtek.com/models/agent_based_models/ CommunityViz Policy Simulator Analysis by Arika Ligmann-Zielinska http://www.uweb.ucsb.edu/~arika/agents/chelan/anim/basic.html Schelling’s segregation Source: Nicholas Gessler UCLA http://www.sscnet.ucla.edu/geog/gessler/borland/