Week 10 Urban Change Assignment
Suhas99
ITS832_Chapter_13.pptxITS 832 Chapter 13
Management of Complex Systems: Toward Agent-Based Gaming for Policy
Information Technology in a Global Economy
Professor Michael Solomon
1
Introduction
Simulating/Managing Social Complex Phenomena
Leadership and Management in Complex Systems
Serious Gaming
Agent-Based Games for Testing Leadership and Management
Single and Multiplayer Settings
Summary and conclusions
Simulating and Managing Social Complex Phenomena
Study of how people interact
Scale prohibits experimentation with real populations
Agent-Base modeling (ABM)
Networked agents
Each agent is an individual
Interaction may modify agent behavior
Managing complex phenomena introduces complexity
Techniques to manage turbulent situations vary
Technique success depends on responding to agent behavior
Which may change based on interactions
Leadership and Management in Complex Systems
Traditional leadership research
Generally focuses on single period in time
Doesn’t address dynamic relationships
Timing of leadership principle application matters
Primary leadership functions
Instructional and regulatory
Developmental
Simulations offer promise to help model leadership in complex systems
Serious Gaming
Applying gaming techniques to real life situations
Flight simulators
Effective for evaluating complex environments
Player must interact with multiple actors and situations
Currently used for side range of training applications
Leadership use
Deterministic – limited scope
ABMs in serious gaming can help understand more complex interactions
Agent-Based Games for Testing Leadership and Management
ABM games with autonomous AI population
Test leadership style effectiveness
Explore which styles work best in different situations
Determine the best choice for a given scenario
Current state of the art is more conceptual
Advances needed in interfaces
Need to allow users to interact with simulation
Keep players engaged
Behavior Impacted by Multiple Factors
How different factors influence one another and result in behavior (opportunity consumption),
which aggregates over all simulated consumers and results in macrolevel outcomes that set the
conditions for a next behavioral cycle. In the consumat approach, the agents have existence needs
(e.g., food, income), social needs (group belongingness and status), and identity needs (personal
preferences, taste). To select a behavior an agent can employ four different types of decisional
strategies, depending on its satisfaction and uncertainty.A satisfied and certain agent will repeat its
previous demand, which captures habitual behavior/routine maintenance. A satisfied but uncertain
agent will imitate the demand of a similar other in its network, which reflects normative compliance
(fashion). A dissatisfied and certain agent will evaluate all possible demands and select the
one providing the best outcomes (optimizing). And finally, a dissatisfied and uncertain agent will
inquire the demands other agents had and copy this demand if the outcomes are expected to be
better (social learning).
7
Single and Multiplayer Games
AI may react poorly to management input
Simulating unexpected consequences of decisions
Overactive AI may degrade realism
Players can dynamically see how decisions affect others
Early simulations allow for only single players
Multiple real players adds more realistic interaction
Players replace some AI
Players interact with each other and AI
8
Summary and Conclusions
ABM-based gaming can measure behaviors of players
Supports experimentation in controlled environment
Study leaderships and management in complex systems
Focus
Interaction with leadership
Interaction with players as a result of leadership action
