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Modelling and Simulations Agent-Based

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Agent-Based

Modelling and Simulations

Agent-Based Modelling

Agostino Poggi

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Complex Systems

♦ Is a highly structured system, which shows structure with variations

♦ Its evolution is very sensitive to initial conditions or to small perturbations

 Number of independent interacting components is large

 There are multiple pathways by which the system can evolve

♦ Is difficult to understand and verify by design or function or both

♦ There are multiple interactions between many different components

♦ Constantly evolves over time

(3)

Numeric Simulation Limits

♦ Equational models have a large number of parameters

♦ Different theories must be used in biology, sociology, economy, ...

♦ Difficulty of the micro/macro transition and difficulty to represent different levels

♦ No representation of the behaviors, but only their overall result (e.g., number of individuals, amount of food, …)

♦ Doesn’t account for the emergence of spatial and time structures (e.g. fish schools or flocks of birds, columns of ants, ...)

(4)

Agent-Based Modelling

♦ Is an approach to modeling systems based on autonomous and interacting agents

♦ Is a bottom-up process

♦ Defines emergent phenomena from micro- behaviors

♦ Supports both optimization models and investigation of a dynamic process

♦ Succeeds where centralized planning and optimization models fail

(5)

Modeling Objectives (1/2)

♦ To understand some systems

 In detail (quantitatively)

 Qualitatively (the relationships between variables of interest)

 To sharpen our intuitions

♦ To forecast or backcast some systems

 Behaviors of participants in the system

 System states (micro, meso or macro-level)

♦ To support intervention in some systems

 To advise participants on their strategies

 To advise owners (e.g., policy-makers) on their management

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Modeling Objectives (2/2)

♦ To create some reality (i.e., models)

 Black-Scholes options-pricing theory

 Game theory for nuclear weapons doctrines

 …

♦ To enable co-ordination between stakeholders (i.e., models as co-ordination artefacts)

 Forecasting models in hedge funds

 Corporate strategy modeling

 Large-scale public policy modeling (national macro- economic models, models of climate change,

communicable disease models)

 …

(7)

Agent Types

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Agent Schools

♦ Artificial intelligence

 Agents as autonomous entities solving problems

♦ Multi-agent systems

 Distributed control of systems

♦ Agent-based modeling (and simulation)

 Simulating (real world) phenomena

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Agent Features

♦ Encapsulated

 Clearly identifiable, with well-defined boundaries and interfaces

♦ Situated in a particular environment

 Receives input through sensors and acts through effectors

♦ Capable of flexible action

 Responds to changes and acts in anticipation

♦ Autonomous

 Has control both over its internal state and over own

behavior, reacts to environmental change and proactively changes its behavior

♦ Designed to meet objectives

 Attempts to fulfill a purpose, solve a problem, or achieve goals

(10)

Agent Model (1/2)

♦ An agent is a persistent thing which has some state and which interacts with other agents, mutually modifying each other’s states

♦ The components of an agent-based model are

 A collection of agents and their states

 Rules governing the interactions of the agents

 Environment within which they live

♦ Interaction among agents is the central point of the simulation

(11)

Agent Model (2/2)

(12)

Environment

(13)

Agent Interaction

(14)

Behavior Ingredients

♦ Rule based

 Nested if-then-else structures

♦ Multi criteria decision making

 Options and weights

♦ Inference engines

 Expert systems, facts (states) and decision heuristics

♦ Machine learning

 Neural networks, deep learning, Bayesian statistics and pattern recognition

♦ Evolutionary computing

 Find a optimal solution in large solution space (genetic algorithms)

(15)

Simulation

♦ Agent models are used as substitutes for another system

♦ Simulations mostly use virtual time

♦ Agents live a in a simulated environment

 Social space

 Virtual 2D/3D space

♦ Time and environment are controllable by the modeler

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Time

♦ Simulations take place in discrete time

♦ Time progresses in ticks

♦ Between two ticks, everything is assumed to

happen in the same time, attempting to simulate the parallelism in real world

♦ As computers are serial processing machines, the order of iterations among agents is very important

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Behavior Vs Goal Oriented Models

♦ Behavior-oriented modeling

 Agents are described by modeling their behaviors

♦ Goal-oriented modeling

 Agents are capable of planning and the modeler described their goal

♦ Choice of modeling strategy strongly depends on application context

(18)

Behavior Oriented Models

♦ Modeler describes agent status and dynamics

♦ Examples of formalisms are activity graphs, crisp/fuzzy rules, constraints, ...

♦ Reactions to perceptions/status changes are defined by the modeler

♦ Can easily accommodate reinforcement learning and evolutionary concepts

♦ Agents’ goal(s) are treated implicitly

♦ Very intuitive mapping with simple biological systems (e.g., insects)

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Goal Oriented Models

♦ Modeler identifies goals of the agents

♦ Agents select a goal and execute actions as a consequence

♦ Reactions are not predefined, but goal dependent

♦ Explicit treatment of goals in the agent behavior, but

 Execution of goal dependent actions can be error-prone

 Leads to significantly more complex model (see Belief- Desire-Intention agent models)

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Development & Use

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Advantages

♦ Allows appropriate modeling capabilities in a number of important disciplines

 Social science, biology, software development, …

♦ Allows to simulate systems that are particularly difficult to treat with traditional approaches

 Emergent phenomena, models with variable structure

♦ Can afford more detail in models

 More realism and micro-validity

♦ Provides an intuitive way of modeling

 Facilitates communication with other fields and enables more researchers to use simulation

(22)

Applicability (1/2)

♦ When there are decisions and behaviors that can be well-defined

♦ When it is important that agents adapt and change their behaviors

♦ When it is important that agents have a dynamic relationship with other agents, and agent

relationships form, change and decay

♦ When it is important that agents form

organizations and when adaptation and learning are important at the organization level

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Applicability (2/2)

♦ When it is important that agents have a spatial component to their behaviors and interactions

♦ When the past is no predictor of the future

because the processes of growth and changes are dynamic

♦ When scale-up to arbitrary levels is important

♦ When process structural change needs to be an endogenous result of the model, rather than an input to the model

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Agent Simulation Vs Macro Simulation (1/3)

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Agent Simulation Vs Macro Simulation (2/3)

 Can deal with multi-agent

systems directly because real agent are represented by

simulated agent

 Facilitates structural validation

 Elegant treatment of variable structures

 Allows to model adaptation and evolution

 Easy to model heterogeneous space and population

 Provides different levels of observation

 Differential equations are a well understood,

established mathematical framework

 Easy to document

 Low number of

parameters, global input- output behavior

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Agent Simulation Vs Macro Simulation (3/3)

 Development of complex models can be very costly

 Difficult to determine minimal model

 Established formalism is missing, difficult to

document

 Calibration problem, i.e., is difficult to find the best

parameter setting for a

model (given a structurally valid model)

 Sensitivity problem, i.e., even small changes may have a large effect

 Assumes homogeneous space and population

 No representation of the

individual and its locality, i.e., no conditional behavior, no adaptive behavior, no flexible interaction

 Can only observe the system as a whole, not its parts

(27)

Applications

Business and Organization Society and Culture Manufacturing operations Ancient civilizations

Supply chains Civil disobedience

Consumer markets Social determinant of terrorism Insurance industry Organizational networks

Economics Military

Artificial financial markets Command and control

Trade networks Force on force

Infrastructure Biology

Electric power markets Population dynamics

Transportation Ecological networks

Hydrogen infrastructure Animal group behavior

Crowds Cell behavior and sub-cell processes

Pedestrian movement Entertainment

(28)

Software (1/2) AgentSheets

AndroMeta AnyLogic Ascape Breve Cormas DEVS EcoLab FLAME JAS

LSD

MAML MATSim MASON MASS

MetaABM MIMOSE MobiDyc

Modelling4all NetLogo

RePast

Repast Simphony SimPack

SimPy SOARS StarLogo SugarScape Swarm

VisualBots Xholon

(29)

Software (2/2)

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