Agent-Based

Modelling and Simulations

Agent-Based Modelling

Agostino Poggi

University of Parma - 2Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 3Agent-Based ModellingProf. Agostino Poggi

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, ...)

University of Parma - 4Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 5Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 6Agent-Based ModellingProf. Agostino Poggi

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)

…

University of Parma - 7Agent-Based ModellingProf. Agostino Poggi

Agent Types

University of Parma - 8Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 9Agent-Based ModellingProf. Agostino Poggi

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 proactivelychanges its behavior

♦Designed to meet objectives Attempts to fulfill a purpose, solve a problem, or achieve

goals

University of Parma - 10Agent-Based ModellingProf. Agostino Poggi

Agent Model (1/2)

♦An agent is a persistent thing which has some stateand 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

University of Parma - 11Agent-Based ModellingProf. Agostino Poggi

Agent Model (2/2)

University of Parma - 12Agent-Based ModellingProf. Agostino Poggi

Environment

University of Parma - 13Agent-Based ModellingProf. Agostino Poggi

Agent Interaction

University of Parma - 14Agent-Based ModellingProf. Agostino Poggi

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)

University of Parma - 15Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 16Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 17Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 18Agent-Based ModellingProf. Agostino Poggi

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)

University of Parma - 19Agent-Based ModellingProf. Agostino Poggi

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)

University of Parma - 20Agent-Based ModellingProf. Agostino Poggi

Development & Use

University of Parma - 21Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 22Agent-Based ModellingProf. Agostino Poggi

Applicability (1/2)

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

♦When it is important that agents adapt and changetheir behaviors

♦When it is important that agents have a dynamicrelationship with other agents, and agent relationships form, change and decay

♦When it is important that agents form organizations and when adaptation and learningare important at the organization level

University of Parma - 23Agent-Based ModellingProf. Agostino Poggi

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 futurebecause 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

University of Parma - 24Agent-Based ModellingProf. Agostino Poggi

Agent Simulation Vs Macro Simulation (1/3)

University of Parma - 25Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 26Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 27Agent-Based ModellingProf. Agostino Poggi

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

Evacuation modelling Movies and games

University of Parma - 28Agent-Based ModellingProf. Agostino Poggi

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

University of Parma - 29Agent-Based ModellingProf. Agostino Poggi

Software (2/2)