Can Complexity Be Captured with Agent- Based Modeling and Simulation? Michael North

Transcription

1 Can Complexity Be Captured with Agent- Based Modeling and Simulation? Michael North

2 The Name Game ABMS is known by many names: ABM: Agent-based modeling or anti-ballistic missile? ABS: Agent-based simulation or anti-lock breaks? IBM: Individual-based modeling or International Business Machines Corporation? ABM, ABS, and IBM are all widely-used acronyms, but ABMS will be used throughout the lectures to avoid confusion with the above mentioned terms 2

3 What is ABMS? ABMS seeks to create electronic laboratories ( e-laboratories ) that allow experimentation with simulated complex systems: ABMS uses sets of agents and frameworks for simulating the agent s decisions and interactions ABMS can show how a system could evolve through time in a way that is difficult to predict from knowledge of the behaviors of the individual agents alone ABMS focuses on individual behavior with the agent rules are often based on theories of the individual such as Rational Individual Behavior, Bounded Rationality or Satisficing Based on these simple types of rules, ABMS can be used to study how patterns emerge ABMS may reveal behavioral patterns at a macro (system) level that are not obvious from an examination of the underlying agent rules alone these patterns are called emergent behavior 3

4 ABMS is Often Used to Model Complex Adaptive Systems A Complex Adaptive System (CAS) is made up of agents that interact and reproduce while adapting to a changing environment Researchers such as John Holland are trying to isolate fundamental causes of adaptation and emergence of system-wide properties in any CAS John Holland has identified the following properties and mechanisms that are common to all CAS: Nonlinearity Diversity Aggregation Flows Tagging Internal models Building blocks ABMS incorporates some of the properties and mechanisms of CAS 4

5 There Are Many Examples of Systems Comprised of Interacting Individuals Economic markets: Producers Distributors Consumers Human immune system: Antibodies Bacteria Viruses Social Systems: People Factions Countries Ecosystems: Species Individuals Hives Flocks 5

6 Where Did ABMS Come From? 6

7 Repast is the One of the Most Popular Among a Range of Available ABMS Toolkits Modeling Power Low High Easy Structured Languages (C, Pascal, etc.) Mathematics Packages (Mathematica, etc.) Spreadsheets NetLogo ccl.northwestern.edu/netlogo/ StarLogo Participatory Simulation Ease of Model Development DIAS IMT flock.cbl.umces.edu/imt Swarm RePast repast.sourceforge.net Ascape Object Oriented Languages (Java, C++, etc.) Selected Example ABMS Toolkits Hard 7

8 What Useful Information Can ABMS Provide? ABMS can help to provide insight into and predictions of agent behaviors ABMS can help to anticipate system dynamics, structures, and possible evolutionary paths including suggesting answers to a variety of questions including the following: What agent rules influence emergent behavior and how do they do so? Will a some types of agents tend to dominate? Will changes come quickly or slowly? Will some systems always be in a state of turbulence? ABMS can be used to help identify disequilibrium situations and their causes ABMS can be used to help identify sources of uncertainty in the underlying system 8

9 ABMS is Complementary to Traditional Techniques Analytics: Analytical modeling seeks to develop rigorous, provable statements about systems Statistical Methods: Statistical modeling specifies how outputs depend on inputs systems are represented as a black boxes Optimization: Optimization modeling seeks to find optimal solutions relative to well-defined objectives and subject to specific constraints Output Input xi QP xk1-10 Discrete Event Simulation: Traditional discrete event simulation modeling represents the inner workings of dynamic processes and moves those representations forward through time at a system level Customer Customer Customer Customer in Transit Wait for first available server Queue Customer Customer Customer Customer in Transit Customer Customer Being Served Server 1 Server 2 Customer 9

10 Analytical Modeling Seeks to Develop Provable Statements About Systems An example is solving a well-posed problem in classical mechanics Difficulties: Analytical models usually focus on global descriptions Analytical models of complex systems can be extremely unwieldy Excessively heroic assumptions are required to create analytically solvable models of many systems Many systems cannot be analytically solved at all 10

11 Statistical Methods Seek Relationships Between Inputs and Outputs Output = f(input 1, Input 2, Input 3,., Input n ) where f is a statistically derived relationship Input System Output Difficulties Output The derived relationships can be brittle Not sensitive to many assumptions or amenable to what-if scenarios Insight into underlying causes is often limited Input 11

12 Optimization Modeling Seeks Best Values Max x 1 and x 2 : (1 - e (x1+x2) ) (Fitness) Subject to : 3 x x 2 < 100 (Size constraint) Difficulties xi Optimization models usually focus on global descriptions Even for well defined problems, finding optimal solutions can be extremely difficult Brittle formulations and solution points can result QP xk

13 Discrete Event Simulation Modeling Represents the Detailed Steps in a Process As It Unfolds Over Time Wait for first available server Customer Customer Customer Queue Customer Customer Customer Customer in Transit Customer in Transit Customer Difficulties Customer Being Served Server 1 2. Customer Begins Transit to Server Server 2 3. Customer Arrives Server, Customer Begins Service, Server Becomes Busy Customer Being Served Customer The Queueing Simulation Has Four Types of Events and Two Activities The emphasis is on fixed processes instead of adaptive actors 1. Customer Arrives to System, Defining Customer the Enters process Queue (ifrepresentation can be difficult since servers busy) there is no a clear delineation between too little and too much detail 4. Customer Completes Service and Departs System, Server Becomes Free 13

14 ABMS is Being Applied to a Wide Range of CAS Social systems: Political systems Small groups Businesses Markets: Financial markets Energy markets Industrial supply chains Infrastructure systems Ecosystems Immune systems Electrical power markets are an example Special Event REGULATOR Generator ISO/RTO/ITP MARKET OPERATION FUNCTIONS ENERGY MARKET ANCILLARY SERVICES MARKET Market Information System Generation Consumers Demand Companies Agents Companies Bilateral Contracts Consumer Contracts and Tariffs Generator Ownership Distribution Transmission Companies Companies POOL MARKETS Generators Distribution Service Territory Consumers Transmission Node Generators ISO/RTO/ITP DISPATCH Transmission FUNCTIONS Link GENERATION COMPANIES and DEMAND DEMAND COMPANIES AGENTS TRANSMISSION TRANSMISSION and and DISTRIBUTION DISTRIBUTION COMPANIES COMPANIES REGULATORY LAYER BUSINESS LAYERS LAYER PHYSICAL LAYER 14

15 Electric Utility Systems Are Evolving Until recently, most electric power systems were managed by regulated, vertically integrated monopolies Several systems, such as those in California and the UK, have implemented open electricity markets that seek to: Promote competition among suppliers Provide consumers with a choice of services The results have been, at best, mixed Many places throughout the nation are planning such changes despite the initial outcomes found in places such California In the old systems, decision-making was centralized within the managing monopolies (constrained system) However, in deregulated systems decision-making is distributed among many competing organizations (agents) 15

16 The Electricity Market CAS Model (EMCAS) Applies ABMS to Model Decentralized Electricity Markets EMCAS is an agent-based electricity market model EMCAS agents take on the roles of individual market participants (generators, distributors, transmission system operators, demand aggregators, customers, regulators) The agents operate in multiple layers within nested time scales (hourly, daily, weekly, monthly, yearly, multi-year) Special Event Generator ISO/RTO/ITP MARKET OPERATION FUNCTIONS Consumer Contracts and Tariffs ISO/RTO/ITP DISPATCH FUNCTIONS Consumers Distribution Companies Distribution Service Territory Demand Agents Companies Bilateral Contracts Transmission Node REGULATOR ENERGY MARKET ANCILLARY SERVICES MARKET Market Information System Generator Ownership Transmission Link Generation Companies Transmission Companies TRANSMISSION TRANSMISSION and and DISTRIBUTION DISTRIBUTION COMPANIES COMPANIES Consumers Generators POOL POOL MARKETS MARKETS Generators GENERATION GENERATION COMPANIES COMPANIES and and DEMAND DEMAND AGENTS COMPANIES REGULATORY LAYER BUSINESS LAYERS LAYER PHYSICAL LAYER 16

17 EMCAS Is Designed to Explore System Possibilities Prediction is not a goal ECMAS is intended to provide ranges of possibilities rather than point answers: The ranges of possibilities are created through multiple simulations The ranges are intended to discover potential weaknesses in electricity markets rather than say whether or not a given agent (company) will actually exploit a given weakness These results can be used by decision makers to form better market policies (market rules) and make better market decisions The focus is on supporting decisions by exploring potential consequences of different market conditions (initial and changing) 17

18 EMCAS Operates at Six Time Scales or Decision Levels 18

19 Live Simulations Were Used to Prototype EMCAS To better understand the requirements of decentralized electricity market modeling, a live electricity market simulation was created The live simulation that was developed used individuals to play the role of generation companies: Each generation company in the market simulation game had three generators Players submitted bids electronically based on publicly posted: Prices Demands Supplies Weather One additional person played the role of the system operator 19

20 The Live Simulation included Exogenous Variability The system operator collected the players bids on a periodic basis and used to them to simulate the operation of an electricity spot market: The simulation calculated market prices and player profits based on internally derived demands, supplies, and weather The actual simulation demands, supply, and weather differed from the publicly posted projections by small random amounts Generating units also suffered from unannounced random outages Several versions of the live simulation where run Results from the live simulation contributed to the design of EMCAS 20

21 Several Types of Agents Illustrate the ABMS Approach Resulting from the Live Simulation Generation company agents illustrate ECMAS approach to modeling competitive decisionmaking Independent System Operator/Regional Transmission Organization (ISO/RTO) agents illustrate ECMAS approach to modeling coordinated behavior Special Event REGULATOR Generator ISO/RTO/ITP MARKET OPERATION FUNCTIONS ENERGY MARKET ANCILLARY SERVICES MARKET Market Information System Generation Consumers Demand Companies Agents Companies Bilateral Contracts Consumer Contracts and Tariffs Generator Ownership Distribution Transmission Companies Companies POOL POOL MARKETS MARKETS Generators Distribution Service Territory Consumers Transmission Node Generators ISO/RTO/ITP DISPATCH Transmission FUNCTIONS Link GENERATION GENERATION COMPANIES COMPANIES and and DEMAND DEMAND AGENTS COMPANIES TRANSMISSION TRANSMISSION and and DISTRIBUTION DISTRIBUTION COMPANIES COMPANIES REGULATORY LAYER BUSINESS LAYERS LAYER PHYSICAL LAYER 21

22 Generation Company Agents Face a Difficult Situation Generation company agents sell generation into each of several markets, with different rules in each market: There is a bilateral contract market that is privately negotiated between individual buyers and sellers There is a spot energy futures market that is centrally cleared There are four backup generation options markets that are each centrally cleared The decision-making process for generation company agents is difficult: The commodity they produce (electric power) cannot typically be stored The power transportation system (the electric grid) follows well understood, but highly complicated, rules Generation company agents have limited knowledge about the other players in the market 22

23 The Power Transportation System Follows Highly Complicated Rules Customer 3 $15/MWh Generator $25/MWh Customer Customer 1 $10/MWh All power in tens of MW Notional example 23

24 Generation Company Agent Decisions are Based On Several Factors (1 of 2) The success of generation company agent decisions are not guaranteed Agents weigh the relative rewards of success against the costs and risks of failure The anticipated success or failure rate is based on experience: Each generation company agent keeps an ongoing private record of historical events (i.e., private memory) including a history of decisions made in the past and these results of those decisions under various supply and demand conditions Information such as system outages, loads, location-based market prices are posted by the ISO on publicly available bulletin board 24

25 Generation Company Agent Decisions are Based On Several Factors (2 of 2) The level of risk that an agent is willing to take is an integral part of its decision-making: More conservative agents that have a lower tolerance for risk may have lower profits but have a steady stream of income More aggressive agents may have the potential for higher profits but experience financial failure if anticipated market behaviors do not come into fruition Some business choices that the generation company agent can consider Bid on contracts or bid into the pool market Bid into the energy market and/or the ancillary services market Adjust/change bid price strategy (production cost, low bid to ensure acceptance, bid high on last portion of capacity, withhold capacity) 25

26 Generation Company Agents Use a Sophisticated Decision-Making Process 26

27 The Decision-Making Processes of Generation Company Agents Allows Learning 27

28 ISO/RTO Agents Act as Coordinators (1 of 2) 28

29 ISO/RTO Agents Act as Coordinators (2 of 2) ISO/RTO agents match buyers and sellers in each of the five public markets ISO/RTO agents approve bilateral contracts to insure physical stability ISO/RTO agents manage payments for the public markets ISO/RTO agents post information on a public bulletin board: Historical generation, outages, weather, loads, and locationbased prices are posted Projected outages, weather, and loads are posted 29

30 An EMCAS Case has been Created Based on the Live Simulation Specific agents representing individual live simulation players were implemented by using EMCAS agent architecture: The strategies of the individual players were determined by asking them to write short descriptions of their approaches after the completion of the live simulation and then following up the writing with a series of focused interviews Once the strategies were determined, agents implementing each of the strategies were programmed The individual agents developed to emulate the live simulation players were run using the same data originally used for the live simulation EMCAS closely matched the results of the six player live simulation 30

31 EMCAS Is Undergoing Thorough Verification and Validation Using a Variety of Techniques Unit testing has and is being preformed for the main components currently in use EMCAS internal algorithms have been and are being reviewed in detail by domain experts EMCAS output has and is being compared to analytically solvable special cases EMCAS output has and is being compared to some historical cases EMCAS is being expanded so the verification and validation is continuing! 31

32 Complexity Can Be Captured with Agent-Based Modeling and Simulation! CAS are structures composed of many components that interact and reproduce while adapting to a changing environment CAS often have numerous nested levels of interaction that span many scales of measurement ABMS can be used to build models for both single-scale and multi-scale CAS 32

33 Can Complexity Be Captured with Agent-Based Modeling and Simulation? Are there additional questions? Michael North 33