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Tutorial on agent-based modelling and simulation

Tutorial on agent-based modelling and simulation
Tutorial on agent-based modelling and simulation

Tutorial on agent-based modelling and simulation

CM Macal 1,2*and MJ North 1,2

1

Center for Complex Adaptive Agent Systems Simulation,Decision &Information Sciences Division,Argonne National Laboratory,Argonne,Il,USA;and 2Computation Institute,The University of Chicago,Chicago,Il,USA

Agent-based modelling and simulation (ABMS)is a relatively new approach to modelling systems composed of autonomous,interacting agents.Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems.Such systems often self-organize themselves and create emergent order.Agent-based models also include models of behaviour (human or otherwise)and are used to observe the collective effects of agent behaviours and interactions.The development of agent modelling tools,the availability of micro-data,and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines.This article provides a brief introduction to ABMS,illustrates the main concepts and foundations,discusses some recent applications across a variety of disciplines,and identi?es methods and toolkits for developing agent models.Journal of Simulation (2010)4,151–162.doi:10.1057/jos.2010.3

Keywords:agent-based modelling and simulation;modelling behaviour;social simulation

1.Introduction

Agent-based modelling and simulation (ABMS)is a relatively new approach to modelling complex systems composed of interacting,autonomous ‘agents’.Agents have behaviours,often described by simple rules,and interactions with other agents,which in turn in?uence their behaviours.By mode-lling agents individually,the full effects of the diversity that exists among agents in their attributes and behaviours can be observed as it gives rise to the behaviour of the system as a whole.By modelling systems from the ‘ground up’—agent-by-agent and interaction-by-interaction—self-organization can often be observed in such models.Patterns,structures,and behaviours emerge that were not explicitly programmed into the models,but arise through the agent interactions.The emphasis on modelling the heterogeneity of agents across a population and the emergence of self-organization are two of the distinguishing features of agent-based simulation as compared to other simulation techniques such as discrete-event simulation and system dynamics.Agent-based model-ling offers a way to model social systems that are composed of agents who interact with and in?uence each other,learn from their experiences,and adapt their behaviours so they are better suited to their environment.

Applications of agent-based modelling span a broad range of areas and disciplines.Applications range from modelling agent behaviour in the stock market (Arthur et al ,1997)and supply chains (Macal,2004a)to predicting the spread of epidemics (Bagni et al ,2002)and the threat of bio-warfare

(Carley et al ,2006),from modelling the adaptive immune system (Folcik et al ,2007)to understanding consumer pur-chasing behaviour (North et al ,2009),from understanding the fall of ancient civilizations (Kohler et al ,2005)to model-ling the engagement of forces on the battle?eld (Moffat et al ,2006)or at sea (Hill et al ,2006),and many others.Some of these applications are small but elegant models,which include only the essential details of a system,and are aimed at developing insights into a social process or behaviour.Other agent-based models are large scale in nature,in which a system is modelled in great detail,meaning detailed data are used,the models have been validated,and the results are intended to inform policies and decision making.These applications have been made possible by advances in the development of specialized agent-based modelling software,new approaches to agent-based model development,the availability of data at increasing levels of granularity,and advancements in computer performance.

Several indicators of the growing interest in agent-based modelling include the number of conferences and work-shops devoted entirely to or having tracks on agent-based modelling,the growing number of peer-reviewed publi-cations in discipline-speci?c academic journals across a wide range of application areas as well as in modelling and simulation journals,the growing number of openings for people specializing in agent-based modelling,and interest on the part of funding agencies in supporting programmes that require agent-based models.For example,a perusal of the programme for a recent Winter Simulation Conference revealed that 27papers had the word ‘agent’in the title or abstract (see https://www.sodocs.net/doc/1113200936.html,/pastprog.htm).

This article provides a brief introduction to ABMS.We illustrate the main concepts of agent-based modelling

*Correspondence:CM Macal,Center for Complex Adaptive Agent Systems Simulation,Decision &Information Sciences Division,Argonne National Laboratory,9700S.Cass Avenue,Argonne,IL 60439-4867,USA.

E-mail:macal@https://www.sodocs.net/doc/1113200936.html, Journal of Simulation (2010)4,151–162r 2010Operational Research Society Ltd.All rights reserved.

1747-7778/10

(Section2),discuss some recent applications across a variety of disciplines(Section3),and identify methods and toolkits for developing agent models(Section4).

2.Agent-based modelling

2.1.Agent-based modelling and complexity

ABMS can be traced to investigations into complex systems (Weisbuch,1991),complex adaptive systems(Kauffman, 1993;Holland,1995),and arti?cial life(Langton,1989), known as ALife(see Macal(2009)for a review of the in?u-ences of investigations into arti?cial life on the development of agent-based modelling and the article by Heath and Hill in this issue for a review of other early in?uences).Complex systems consist of interacting,autonomous components; complex adaptive systems have the additional capability for agents to adapt at the individual or population levels. These collective investigations into complex systems sought to identify universal principles of such systems,such as the basis for self-organization,emergent phenomenon,and the origins of adaptation in nature.ABMS began largely as the set of ideas,techniques,and tools for implementing computational models of complex adaptive systems.Many of the early agent-based models were developed using the Swarm modelling software designed by Langton and others to model ALife(Minar et al,1996).Initially,agent behaviours were modelled using exceedingly simple rules that still led to exceedingly complex emergent behaviours. In the past10years or so,available agent-based model-ling software tools and development environments have expanded considerably in both numbers and capabilities. Following the conventional de?nition of simulation, we use the term ABMS in this article to refer to both agent-based simulation,in which a dynamic and time-dependent process is modelled,and more general kinds of agent-based modelling that includes models designed to do optimization(see,eg,Olariu and Zomaya,2006)or search (see,eg,Hill et al,2006).For example,particle swarm optimization and ant optimization algorithms are both inspired by agent-based modelling approaches and are used to achieve an end(optimal)state rather than to investigate a dynamic process,as in a simulation.

2.2.Structure of an agent-based model

A typical agent-based model has three elements:

1.A set of agents,their attributes and behaviours.

2.A set of agent relationships and methods of interaction:

An underlying topology of connectedness de?nes how and with whom agents interact.

3.The agents’environment:Agents interact with their

environment in addition to other agents.

A model developer must identify,model,and program these elements to create an agent-based model.The structure of a typical agent-based model is shown in Figure1.Each of the components in Figure1is discussed in this section.A computational engine for simulating agent behaviours and agent interactions is then needed to make the model run.An agent-based modelling toolkit,programming language or other implementation provides this capability.To run an agent-based model is to have agents repeatedly execute their behaviours and interactions.This process often does,but is not necessarily modelled to,operate over a timeline,as in time-stepped,activity-based,or discrete-event simulation

structures.

Figure1The structure of a typical agent-based model,as in Sugarscape(Epstein and Axtell,1996). 152Journal of Simulation Vol.4,No.3

2.3.Autonomous agents

The single most important de?ning characteristic of an agent is its capability to act autonomously,that is,to act on its own without external direction in response to situations it encounters.Agents are endowed with behaviours that allow them to make independent decisions.Typically,agents are active,initiating their actions to achieve their internal goals, rather than merely passive,reactively responding to other agents and the environment.

There is no universal agreement in the literature on the precise de?nition of an agent beyond the essential property of autonomy.Jennings(2000)provides a computer science de?nition of agent that emphasizes the essential character-istic of autonomous behaviour.Some authors consider any type of independent component(software,model,indi-vidual,etc)to be an agent(Bonabeau,2001).In this view,a component’s behaviour can range from simplistic and reactive‘if-then’rules to complex behaviours modelled by adaptive arti?cial intelligence techniques.Other authors insist that a component’s behaviour must be adaptive,able to learn and change its behaviours in response to its experiences,to be called an agent.Casti(1997)argues that agents should contain both base-level rules for behaviour and higher-level rules that are in effect‘rules to change the rules’.The base-level rules provide more passive responses to the environment,whereas the‘rules to change the rules’provide more active,adaptive capabilities.

From a practical modelling standpoint,based on how and why agent-models are actually built and described in applications,we consider agents to have certain essential characteristics:

An agent is a self-contained,modular,and uniquely identi?able individual.The modularity requirement im-plies that an agent has a boundary.One can easily determine whether something is part of an agent,is not part of an agent,or is a shared attribute.Agents have attributes that allow the agents to be distinguished from and recognized by other agents.

An agent is autonomous and self-directed.An agent can function independently in its environment and in its interactions with other agents,at least over a limited range of situations that are of interest in the model.An agent has behaviours that relate information sensed by the agent to its decisions and actions.An agent’s information comes through interactions with other agents and with the environment.An agent’s behaviour can be speci?ed by anything from simple rules to abstract models,such as neural networks or genetic programs that relate agent inputs to outputs through adaptive mechanisms.

An agent has a state that varies over time.Just as a system has a state consisting of the collection of its state variables,an agent also has a state that represents the essential variables associated with its current situation.An

agent’s state consists of a set or subset of its attributes.

The state of an agent-based model is the collective states of all the agents along with the state of the environment.

An agent’s behaviours are conditioned on its state.As such,the richer the set of an agent’s possible states,the richer the set of behaviours that an agent can have.In an agent-based simulation,the state at any time is all the information needed to move the system from that point forward.

An agent is social having dynamic interactions with other agents that in?uence its behaviour.Agents have protocols for interaction with other agents,such as for communica-tion,movement and contention for space,the capability to respond to the environment,and others.Agents have the ability to recognize and distinguish the traits of other agents.

Agents may also have other useful characteristics:

An agent may be adaptive,for example,by having rules or more abstract mechanisms that modify its behaviours.

An agent may have the ability to learn and adapt its behaviours based on its accumulated experiences.Learn-ing requires some form of memory.In addition to adaptation at the individual level,populations of agents may be adaptive through the process of selection,as individuals better suited to the environment proportio-nately increase in numbers.

An agent may be goal-directed,having goals to achieve (not necessarily objectives to maximize)with respect to its behaviours.This allows an agent to compare the out-come of its behaviours relative to its goals and adjust its responses and behaviours in future interactions.

Agents may be heterogeneous.Unlike particle simulation that considers relatively homogeneous particles,such as idealized gas particles,or molecular dynamics simulations that model individual molecules and their interactions, agent simulations often consider the full range of agent diversity across a population.Agent characteristics and behaviours may vary in their extent and sophistication, how much information is considered in the agent’s deci-sions,the agent’s internal models of the external world, the agent’s view of the possible reactions of other agents in response to its actions,and the extent of memory of past events the agent retains and uses in making its decisions.Agents may also be endowed with different amounts of resources or accumulate different levels of resources as a result of agent interactions,further differen-tiating agents.

A typical agent structure is illustrated in Figure2.In an agent-based model,everything associated with an agent is either an agent attribute or an agent method that operates on the agent.Agent attributes can be static,not change-able during the simulation,or dynamic,changeable as the CM Macal and MJ North—Tutorial on agent-based modelling and simulation153

simulation progresses.For example,a static attribute is an agent’s name;a dynamic attribute is an agent’s memory of past interactions.Agent methods include behaviours,such as rules or more abstract representations such as neural net-works,which link the agent’s situation with its action or set of potential actions.An example is the method that an agent uses to identify its neighbours.

A theory of agent behaviour for the situations or contexts the agent encounters in the model is needed to model agent behaviour.One may begin with a normative model in which agents attempt to optimize pro?ts,utility,etc,as a starting point for developing a simpler,more descriptive,but realistic,heuristic model of behaviour.One may also begin with a behavioural model if there is available behavioural theory and empirical data to support the application.For example,numerous theories and empirically based heuristics exist for modelling consumer shopping behaviour.These could be implemented and compared in an agent-based model.Cognitive science and related disciplines focus on agents and their social behaviours (Sun,2006).Behavioural modelling frameworks such as BDI (Belief-Desire-Intent)combine modal and temporal logics as the basis for reactive planning and agent action selection (Wooldridge,2000).In agent-based modelling applications in which learning is important,theories of learning by individual agents or collectives of agents become important.The ?eld of machine learning is another source of learning algorithms for recognizing patterns in data (such as data mining)through techniques such as supervised learning,unsupervised learn-ing,and reinforcement learning (Alpaydy n,2004;Bishop,2007).Genetic algorithms (Goldberg,1989)and related techniques such as learning classi?er systems (Holland et al ,2000)are also commonly used in agent-based models.

2.4.Interacting agents

Agent-based modelling concerns itself with modelling agent relationships and interactions as much as it does modelling agent behaviours.The two primary issues of modelling agent interactions are specifying who is,or could be,connected to who,and the mechanisms of the dynamics of the interactions.Both aspects must be addressed in developing agent-based models.

One of the tenets of complex systems and agent-based modelling is that only local information is available to an agent.Agent-based systems are decentralized systems.There is no central authority that either pushes out globally available information to all agents or controls their beha-viour in an effort to optimize system performance.Agents interact with other agents,but not all agents interact directly with all the other agents all the time,just as in real-world systems.Agents typically interact with a subset of other agents,termed the agent’s neighbours .Local information is obtained from interactions with an agent’s neighbours (not any agent or all agents)and from its localized environment (not from any part of the entire environment).Generally,an agent’s set of neighbours changes rapidly as a simulation proceeds and agents move through space.

How agents are connected to each other is generally termed an agent-based model’s topology or connectedness.Typical topologies include a spatial grid or network of nodes (agents)and links (relationships).A topology describes who transfers information to whom.In some applications,agents interact according to multiple topologies.For example,a recent agent-based pandemic model has agents interacting over a spatial grid to model physical contact as agents go through daily activities and possibly pass on infections.Agents also are members of social networks that model the likelihood of contact with relatives and friends.

An agent’s neighbourhood is a general concept applicable to whatever agent spaces are de?ned in the model.For example,an agent could interact only with its neighbours located close-by in physical (or geographical)space as well as neighbour agents located close-by in its social space as speci?ed by the agent’s social network.

Originally,spatial agent-based models were implemented in the form of cellular automata (CA).Conway’s Game of Life (Gardner,1970)is a good example.CA represent agent interaction patterns and available local information by using a grid or lattice environment.The cells immediately surrounding an agent are its neighbourhood.Each cell can be interpreted as an agent that interacts with a ?xed set of neighbouring cells.The cell (agent)state is either ‘on’or ‘off’at any time.Most early spatial agent-based models had the form of a CA.Epstein and Axtell’s Sugarscape model is an example (Epstein and Axtell,1996).In Sugarscape ,the topology was more complex than in a simple CA.Agents were mobile and able to move from cell to cell.The grid essentially became the agents’environment.Agents

were

Figure 2A typical agent.

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able to acquire resources from the environment that were distributed spatially across the grid.

Other agent interaction topologies are now commonly used for modelling agent interactions(Figure3).In the CA model,agents move from cell to cell on a grid and no more than a single agent occupies a cell at one time.The von Neumann‘5-neighbour’neighbourhood is shown in Figure3a;the‘9-neighbour’Moore neighbourhood is also common.In the Euclidean space model,agents roam in two, three or higher dimensional spaces(Figure3b).Networks allow an agent’s neighbourhood to be de?ned more generally. For the network topology,networks may be static or dyna-mic(Figure3c).In static networks,links are pre-speci?ed and do not change.For dynamic networks,links,and possibly nodes,are determined endogenously according to the mechanisms programmed in the model.In the geographic information system(GIS)topology,agents move from patch to patch over a realistic geo-spatial landscape (Figure3d).In the‘soup’,or aspatial model,agents have no location because it is not important(Figure3e);pairs of agents are randomly selected for interaction and then returned to the soup as candidates for future selection. Many agent-based models include agents interacting in multiple topologies.

2.5.Agent environment

Agents interact with their environment and with other agents.The environment may simply be used to provide information on the spatial location of an agent relative to other agents or it may provide a rich set of geographic information,as in a GIS.An agent’s location,included as a dynamic attribute,is sometimes needed to track agents as they move across a landscape,contend for space, acquire resources,and encounter other https://www.sodocs.net/doc/1113200936.html,plex environmental models can be used to model the agents’environment.For example,hydrology or atmospheric dispersion models can provide point location-speci?c data on groundwater levels or atmospheric pollutants,respec-tively,which are accessible by agents.The environment may thus constrain agent actions.For example,the environment in an agent-based transportation model would include the infrastructure and capacities of the nodes and links of the road network.These capacities would create congestion effects(reduced travel speeds)and limit the number of agents moving through the transportation network at any given time.

3.Agent-based modelling applications

3.1.The nature of agent-based model applications Agent-based modelling has been used in an enormous variety of applications spanning the physical,biological, social,and management sciences.Applications range from modelling ancient civilizations that have been gone for hundreds of years to modelling how to design new markets that do not currently exist.Several agent-based modelling applications are summarized in this section,but the list is only a small sampling.Several of the papers

covered Figure3Topologies for agent relationships and social interaction.

CM Macal and MJ North—Tutorial on agent-based modelling and simulation155

here make the case that agent-based modelling,versus other modelling techniques is necessary because agent-based models can explicitly model the complexity arising from individual actions and interactions that exist in the real world.

Agent-based model structure spans a continuum,from elegant,minimalist academic models to large-scale decision support systems.Minimalist models are based on a set of idealized assumptions,designed to capture only the most salient features of a system.Decision support models tend to serve large-scale applications,are designed to answer real-world policy questions,include real data,and have passed appropriate validation tests to establish credibility.

3.2.Applications overview

Troisi et al(2005)applied agent-based simulation to model molecular self-assembly.Agents consist of individual mole-cules,and agent behaviours consist of the physical laws of molecular interaction.Such agent-based modelling approaches have found use in investigating pattern forma-tion in the self-assembly of nano-materials,in explaining self-organized patterns formed in granular materials,and other areas.

In the biological sciences,agent-based modelling is used to model cell behaviour and interaction,the workings of the immune system,tissue growth,and disease processes. Generally,authors contend that agent-based modelling offers bene?ts beyond traditional modelling approaches for the problems studied and use the models as electronic labo-ratories as an adjunct to traditional laboratories.Cellular automata are a natural application for modelling cellu-lar systems(Alber et al,2003).One approach uses the cellular automata grid to model structures of stationary cells comprising a tissue matrix.Mobile cells consisting of pathogens and antibodies are agents that diffuse through and interact with tissue and other co-located mobile cells. The Basic Immune Simulator is built on a general agent-based framework to model the interactions between the cells of the innate and adaptive immune system(Folcik et al, 2007).Approaches for modelling the immune system have inspired several agent-based models of intrusion detection for computer networks(Azzedine et al,2007)and modelling the development and spread of cancer(Preziosi,2003). Emonet et al(2005)developed an agent-based simulator AgentCell for modelling the chemotaxis processes for motile behaviour of the E.Coli bacteria.In this multi-scale simulation,agents are modelled as individual molecules as well as whole cells.The model is used to study how the range of natural cell diversity at the molecular level is responsible for the observed range of cell movement behaviours.

In ecology,agent-based modelling is used to model diverse populations of individuals and their interactions.Mock and Testa(2007)develop an agent-based model of predator-prey relationships between transient killer whales and threatened marine mammal species(sea lions and sea otters)in Alaska.The authors state that until now only simplistic,static models of killer whale consumption had been constructed because of the fact that the interactions between transient killer whales and their marine mammal prey are poorly suited to classical predator-prey modelling approaches. Agent-based epidemic and pandemic models incorporate spatial and network topologies to model people’s realistic activity and contact patterns(Carley et al,2006;Epstein et al,2007).The focus is on understanding tipping point conditions that might lead to an epidemic and identifying possible mitigation measures.These models explicitly con-sider the role of people’s behaviour and interactions through social networks as they affect the spread of infectious diseases.

Computational social science is an emerging?eld that combines modelling and simulation with the social science disciplines(Sallach and Macal,2001).Agent-based models have been developed in the?elds of economics,socio-logy,anthropology,and cognitive science.Various social phenomena have been investigated using agent-based models that are not easily modelled using other approaches(Macy and Willer,2002;Gilbert and Troitzsch,2005).Theoretical applications include social emergence(Sawyer,2005),the emergence of cooperation(Axelrod,1997),the generation of social instability(Epstein,2002),and the collective beha-viour of people in crowds(Pan et al,2007).Sakoda(1971) formulated one of the?rst social agent-based models,the Checkerboard Model,which relied on a cellular automaton. Using a similar approach,Schelling developed a model of housing segregation in which agents represent homeowners and neighbours,and agent interactions represent agents’perceptions of their neighbours(Schelling,1978).Schelling showed that housing segregation patterns can emerge that are not necessarily implied or consistent with the objectives of the individual agents.Epstein and Axtell(1996)extended the notion of modelling people to growing entire arti?cial societies through agent-based simulation in the grid-based Sugarscape model.Sugarscape agents emerged with a variety of characteristics and behaviours,highly suggestive of a realistic,although rudimentary and abstract,society.These early grid-based models with limited numbers of social agents are now being extended to large-scale simulations over realistic social spaces such as social networks and geographies through real-time linkages with GIS.

In many economic models based on standard micro-economic theory,simplifying assumptions are made for ana-lytical tractability.These assumptions include(1)economic agents are rational,which implies that agents have well-de?ned objectives and are able to optimize their behaviour, (2)economic agents are homogeneous,that is,agents have identical characteristics and rules of behaviour,(3)the system experiences primarily decreasing returns to scale from economic processes(decreasing marginal utility,decreasing marginal productivity,etc),and(4)the long-run equilibrium state of the system is the primary information of interest.

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Agent-based modelling allows relaxing the standard assump-tions of classical economics(Arthur et al,1997)so the transient states that are encountered along the way to equilibrium can be investigated(Axtell,2000).This interest has spawned the?eld of Agent-based Computational Economics(Tesfatsion,2002;Tesfatsion and Judd,2006). Much applicable work is being done on understanding how people make decisions in actual situations in such ?elds as behavioural economics and neuro-economics.This work offers promise in building better empirically based models of agent behaviours that consider rational factors and emotion.

Agent-based models are being used to analyse markets, both existing and hypothetical.Charania et al(2006)use agent-based simulation to model possible futures for a market in sub-orbital space tourism.Each agent is a repre-sentation of an entity within the space industry.Tourism companies seek to maximize pro?ts while they compete with other companies for sales.Customers evaluate the products offered by the companies according to their individual tastes and preferences.Lo pez-Sa nchez et al(2005)developed a multi-agent based simulation of news digital markets adapting traditional business models to investigate market dynamics.Yin(2007)developed an agent-based model of Rocky Mountain tourism applied to the town of Breckenridge, Colorado;the model was used to explore how homeowners’investment and reinvestment decisions are in?uenced by the level of investment and amenities available in their neighbourhoods.Tonmukayakul(2007)developed an agent-based computational economics model to study market mechanisms for the secondary use of the radio spectrum. Using transaction cost economics as the theoretical frame-work,the model was used to identify the condi-tions for when and why the secondary use market could emerge and what form it might take.

Archaeology and anthropology are making use of large-scale agent-based modelling by providing an experimental virtual laboratory for long-vanished civilizations.Kohler et al(2005)employed large-scale agent-based simulations based on archaeological evidence to understand the social and cultural factors responsible for the disappearance of the ancient Pueblo in some parts of the south-western USA. Wilkinson et al(2007)used agent-based modelling to understand the growth and decline of ancient Mesopota-mians.

Agent-based models of many real-world systems tend to consist of a mix of physical components(modelled as agents) and social agents,termed‘socio-technic’systems.Examples of such systems for which large-scale agent-based models have been developed include traf?c,air traf?c control, military command and control and net-centric operations, physical infrastructures and markets,such as electric power and integrated energy markets.For example,Cirillo et al (2006)used an agent-based approach to model the Illinois electric power markets under conditions of deregulation in an effort to anticipate likely effects on electricity prices and reliability.

This special issue adds to the growing list of agent-based model applications.Qu et al use their model of egg plant growth to promote understanding of the interactions between plant architecture and physiological processes. Chen and Hardoon use their model to examine cell division and migration in the colonic crypt to better understand the mechanisms of tumorigenesis.

4.Methods for agent-based modelling

4.1.Agent model design

When developing an agent-based model,it is useful to ask a series of questions,the answers to which will lead to an initial model design:

1.What speci?c problem should be solved by the model?

What speci?c questions should the model answer?What value-added would agent-based modelling bring to the problem that other modelling approaches cannot bring?

2.What should the agents be in the model?Who are the

decision makers in the system?What are the entities that have behaviours?What data on agents are simply descriptive(static attributes)?What agent attributes would be calculated endogenously by the model and updated in the agents(dynamic attributes)?

3.What is the agents’environment?How do the agents

interact with the environment?Is an agent’s mobility through space an important consideration?

4.What agent behaviours are of interest?What decisions do

the agents make?What behaviours are being acted upon?

What actions are being taken by the agents?

5.How do the agents interact with each other?With the

environment?How expansive or focused are agent interactions?

6.Where might the data come from,especially on agent

behaviours,for such a model?

7.How might you validate the model,especially the agent

behaviours?

Answering these questions is an essential part of the agent-based model design process.There are a variety of approaches to designing and implementing agent-based models.North and Macal(2007)discuss both design metho-dologies and selected implementation environments in depth.Marsh and Hill(2008)offer an initial methodology for de?ning agent behaviours in an application for unmanned autonomous vehicles.Overall,bottom-up,highly iterative design methodologies seem to be the most effective for practical model development.Modern software(and model)development practices dictate that model design be independent of model implementation.That is,a good CM Macal and MJ North—Tutorial on agent-based modelling and simulation157

software(model)design should be able to be implemented in whatever computer language or coding scheme is selected. The communication of a model,its design assumptions, and detailed elements is essential if models are to be under-stood and reused by others than their original developers. Grimm et al(2006)present a proposed standard protocol for describing agent-based and related models as a?rst step for establishing a more detailed common format.

4.2.Agent model implementation

Agent-based modelling can be done using general,all-purpose software or programming languages,or it can be done using specially designed software and toolkits that address the special requirements of agent modelling.Agent modelling can be done in the small,on the desktop,or in the large, using large-scale computing cluster,or it can be done at any scale in-between these extremes.Projects often begin small, using one of the desktop ABMS tools,and then grow in stages into the larger-scale ABMS toolkits.Often one begins developing their?rst agent model using the approach that one is most familiar with,or the approach that one?nds easiest to learn given their background and experience.

We can distinguish implementation alternatives to build-ing agent-based models on the basis of the software used. Spreadsheets,such as Microsoft Excel,in many ways offer the simplest approach to modelling.It is easier to develop models with spreadsheets than with many of the other tools, but the resulting models generally allow limited agent diversity,restrict agent behaviours,and have poor scalability compared to the other approaches.Some macro-level programming is also needed using the VBA language. General computational mathematics systems such as MATLAB and Mathematica,which many people may be already familiar with,can also be used quite successfully; however,these systems provide no speci?c capabilities for modelling agents.General programming languages such as Python,Java,and C++,and C also can be used,but development from scratch can be prohibitively expensive given that this would require the development of many of the available services already provided by specialized agent modelling tools.Most large-scale agent-based models use specialized tools,toolkits,or development environ-ments based on reasons having to do with usability,ease of learning,cross-platform compatibility,and the need for sophisticated capabilities to connect to databases,graphical user interfaces and GIS.

4.3.Agent modelling services

Regardless of the speci?c design methodology that is selected,a range of services is commonly required for implementing large-scale models that include real data and geo-spatial environments,which are becoming more pre-valent.Some of the more common capabilities include project speci?cation services;agent speci?cation services; input data speci?cation and storage services;model execu-tion services;results storage and analysis services;and model packaging and distribution services.

Project speci?cation services provide a way for modellers to identify which sets of resources(eg?les)constitute each model.There are three common approaches,depending on how much support the implementation environment pro-vides for the modeller:(1)the library-oriented approach,(2) the integrated development environment(IDE)approach, and(3)the hybrid approach.

In the library-oriented approach to project speci?cation, the agent modelling tool consists of a library of routines organized into an application programming interface(API). Modellers create models by making a series of calls to the various functions within the modelling toolkit.It is the responsibility of modellers to ensure that the correct call sequences are used and that all of the required?les are present.In exchange,modellers have great?exibility in the way that they de?ne their models.Examples include the Java archives(JAR)used by Repast for Java(North et al,2006; ROAD,2009)or MASON(GMU,2009);the binary libraries used by Swarm(SDG,2009);and the Microsoft .NET assemblies used by Repast for the https://www.sodocs.net/doc/1113200936.html, framework(North et al,2006;ROAD,2009).

The IDE approach to project speci?cation uses a code or model editing program to organize model construction. IDE’s also provide a built-in mechanism to compile or interpret and then execute models.There are several options including combined‘one?le’IDEs,factored multiple-?le IDEs,and hybrid https://www.sodocs.net/doc/1113200936.html,bined‘one?le’IDEs use a single?le to describe each model.An example is NetLogo (Wilensky,1999;NetLogo,2009).These systems are often quite easy to initially learn and use,but do not always scale well to larger and more complex models as compared to the other project speci?cation approaches.The scalability issues include dif?culties supporting team development,dif?culties with editing increasingly large model?les,and dif?cul-ties in organizing and reorganizing model code as it grows. Factored multiple-?le IDEs use a set of?les to describe each model.They usually include some type of built-in?le manager along with the editor.Factored multiple-?le IDEs can use either custom development environments which are specially built for a given agent platform;standards-based environments such as Eclipse(Eclipse Foundation,2009),or a mixture of custom and standards-based environments. Support for features like team development(ie two or more modellers simultaneously creating a model),version control (ie automated tracking of code changes),and refactoring(ie automated tools for reorganizing code)helps to make these environments more powerful than typical combined‘one?le’IDEs.In many cases,these environments require more knowledge to use than‘one?le’IDEs but they also tend to scale more effectively.However,they may be less?exible

158Journal of Simulation Vol.4,No.3

than hybrid systems in the more extreme cases of model size and complexity.

The hybrid approach to project speci?cation allows modellers to use the environment as either a stand-alone library or a factored multiple-?le IDE.Examples include Repast Simphony(North et al,2007;ROAD,2009)and AnyLogic(XJ Technologies,2009).In exchange for this added?exibility,these environments may require more knowledge to use than other types of IDEs but they also tend to scale the most effectively.

Agent speci?cation services provide a means for modellers to de?ne the attributes and behaviours of agents.These services can use general purpose languages such as Cttor Java;textual domain-speci?c languages(DSLs)such as Mathematica or MATLAB(Macal,2004b);or visual DSLs such as the Repast Simphony?owchart shown in Figure4. Along with or included in the language features,some implementation environments provide special support for features such as adaptation and learning(eg neural networks);optimization(eg genetic algorithms);social net-works;geographical information systems(GIS);and systems dynamics.

Input data speci?cation and storage services allow users to setup and save data that de?nes model runs.Input data setup can be done visually by pointing and clicking to create agents,by using custom programs to create agents in speci?ed patterns,or by using external input data?les in customized or standardized?le formats.The standard storage formats can include extensible markup language (XML)?les,spreadsheets,databases,or GIS?les.

Some Figure4A Repast Simphony agent behaviour?owchart.

CM Macal and MJ North—Tutorial on agent-based modelling and simulation159

systems also allow‘checkpointing’,which is saving and restoring the current state of a model at any time during execution.

Model execution services provide a means for model users to run and interact with simulations.Interactive execution can include viewing and modifying the attributes of agents (ie agent‘probing’);displaying agents in two and three dimensions;and running models without visual displays to quickly generate data(ie‘batch execution’).Batch execution can include the execution of multiple model runs on one local computer or on clusters of computers.

Results storage and analysis services allow model users to conveniently examine the results of individual model runs or sets of runs.Major analysis mechanisms include visualiza-tion,data mining,statistics,and report generation.Most implementation environments allow modellers to produce output text or binary?les during execution,primarily using programming.These output?les can then be manually read into separate external analysis tools.Some implementation environments such as Repast Simphony(North et al,2007; ROAD,2009)and AnyLogic(XJ Technologies,2009) include either built-in analysis tools or point-and-click mechanisms to create output?les and directly invoke external analysis tools.

Model packaging and distribution services allow mod-ellers to disseminate completed models to end users.There are a range of methods for packaging models including embedded-platform packaging,IDE-based packaging, and stand-alone packaging.Once models are packaged there are several ways to distribute the results including?le-based distribution,installer-based distribution,and web-based execution.In principle,any of the distribution options can be used with any of the packaging approaches. Embedded-platform packaging places models within larger surrounding software systems.This kind of packaging is often used for models that are built using the library project speci?cation approach.This approach usually requires substantial software development knowledge. IDE-based packaging occurs when a model is developed using the IDE project speci?cation approach and is then disseminated by distributing copies of the IDE with the model inside.This approach usually allows users to examine and change the model when they receive it.It also sometimes requires greater skill on the user’s part compared to the other packaging approaches since IDEs can be somewhat complex.Stand-alone packaging binds a model into a program separate from the development environment that was used to create it.This new program, commonly called the‘runtime version’of the model,can be distributed to end users.This approach is usually the simplest for users who want to execute the model but not examine or change the code.

File-based distribution places the?les that constitute the model in a user accessible location such as a CD,DVD,?le server,or website.These?les can be individually accessed or distributed in a compressed or uncompressed archive. Installer-based distribution uses a custom program which copies the model onto the user’s computer and then con?gures it for execution.Installers usually have graphical wizard-based interfaces that make installation more reliable than for the other distribution approaches because of the ability of the installation software to automatically?x common con?guration issues.Web-based execution embeds a packaged model into a web page for execution from within a browser.Web-based execution is differentiated from simply making raw?les or an installer available from a website in that it requires models to execute from within a browser or browser plug-in rather than simply being downloaded and installed from an online source.Web-based execution is often the easiest and fastest distribution method for users.However,reliability can suffer because of the varying functionality of the wide range of browsers and browser plug-ins that are in common use today.

This section shows that there is a wide range of ways to implement agent-based models.When evaluating agent modelling tools,it should be noted that no one approach is universally better for all situations.Rather,different kinds of implementation approaches and environments have various strengths and weakness depending on the modelling questions of interest.Furthermore,it is common to use different tools during different stages of model development. For example,a modeller might start with a combined‘one ?le’IDE for initial model prototyping and then later transition to a factored multiple-?le IDE as the model scales up in size and complexity.Therefore,the existing range of tools can best be thought of as a portfolio of options from which good selections can be made for each modelling question and stage.

5.Summary and conclusions

ABMS is a new approach to modelling systems comprised of autonomous,interacting agents.There are a growing num-ber of agent-based applications in a variety of?elds and disciplines.ABMS is particularly applicable when agent adaptation and emergence are important considerations. Many agent-based software and toolkits have been devel-oped and are widely used.A combination of several syner-gistic factors is moving ABMS forward rapidly.These factors include the continuing development of specialized agent-based modelling methods and toolkits,the widespread application of agent-based modelling,the mounting collec-tive experience of the agent-based modelling community,the recognition that behaviour is an important missing element in existing models,the increasing availability of micro-data to support agent-based models,and advances in computer performance.Taken together,these factors suggest that ABMS promises to have far-reaching effects into the future on how businesses use computers to support decision-

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making,government uses models to make and support policy,and researchers use electronic laboratories to further their research.

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Received17November2009;

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162Journal of Simulation Vol.4,No.3

密室逃脱计划书

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小游戏----密室逃脱

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小游戏----密室逃脱

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5、左转,在斜对着蓝门处,(就是那个有窟窿的蓝门,不是一开始的那个门)。点头顶的 灯罩,在灯罩上发现“罗”字图片(小红块,仔细点),在墙上发现“ 蜜”字图片(小白块)。 6、左转,在小冰箱里发现“ 波”字图片和一罐啤酒(啤酒先不要拿,因为骷髅要冰冻的)。在旁边的红桶里发现张纸,打开是些黑头发。记住:点装黑头发的纸,然后点击查看物体,放大纸,多点几下纸,然后就可以在红桶底下得到打火机。 7、用钥匙开红笔记本打开,在日记中可以找到女孩的生日,那就是开骷髅脚上的密码。(通常会是在日记的第四页,里面有一句话“The day after tomorrow is her birthday”,这一天的简单写法 就是密码。你先看你所翻到的那页日记上是几月几号,再在那个日期后加两天,就是小女孩子的 生日,那一天的简写就是密码。(如9月10日,简写就是0910,这就是密码),记下密码。继续翻日记,翻到最后得到一张CD。 8、放大装有头发的纸,点打火机,再点纸,可以烧出东西来,放大药瓶,里面有两粒药,一 张写有箴言的纸。放大绿十字,按照箴言的顺序位置和烧出来得数字相对应(形位相同),把字( 6张图片)填在十字架状物体上(位置随机的),放对会变成个四方体,放下暂不用。 9、在壁橱的第3格,有香炉和磬(旁边红的是槌),把装有CD的CD盒放在香炉后面,把香放香炉左,罐啤放香左(要冰镇的啊,冰一两分钟就可以拿出来了,先把啤酒从冰箱拿出来,点显 百度攻略&口袋巴士提供 2

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现在,非常流行真人密室逃脱的游戏,让你进入密室里面,想尽一切办法逃出来。很多人不明白这个怎么玩,小编今日以自己的经验为你介绍。 方法/步骤 1首先,你在网上查一下在你所在的地方,有什么真人的密室。最好去的人比较多,年龄是不受限制的,但是尽量不要带16岁以下的,带去了没用,而且又花费了一张票钱。 2真人密室有很多主题。比如,诅咒;医院;梦游等等。还有很多电影的,比如,生化危机;电锯惊魂;盗墓,都是很火热的主题。所谓,主题,就是说密室里面的道具、背景、风格是哪种。 3选好主题之后。价格方面,其实所以密室都是差不多的价格,除非它的规模很大。价格基本在40到65一人,团购价,如果是选择在网上团购,那么要先打电话预约时间。注意:如果你明天要去,最好是提前半天打电话。这种真人游戏需要预约。 4打电话联系,会定下你们的时间,要求是提前15分钟到她们店里。如果你不选择网上团购,那么就到现场再付费,但是也是需要预约的。因为,真人密室非常受人追捧。而且,每场游戏时间都是一个小时。 5一定要提前去。去了之后,店内都有桌游可以随便玩,有饮料供应。所以也不必担心无聊。去之前最好少带随身物品,女士包包,都要少带。进去之前,工作人员会让你交出手机(有干扰),放在保险箱里。大可放心。 6进去之后,会有纸张写密室的故事背景。进去之后,一定要注意看周围的东西。一般来说,密室里面大多都是机械化的东西,像空调就不用找了,有纸张会提示你们什么东西不用去找。然后你们就要去找东西,也就是线索。手能够得到的地方都去摸一摸。 7但是这个也不是完全凭找东西就可以通关的。要靠线索去联想怎么回事。而且,如果没有用到的东西一定要把它带在身上,很多没用到的下一关就有可能用。工作人员会发给你传呼器,是给你提示用的,你们想不出来,就可以问问他。 选择加盟魔魔岛密室逃脱的5大理由 理由1:逼真代入感,如侦探小说一般,抽丝剥茧,环环相扣的逻辑,挑战你的IQ与思维。理由2:中型线索类密室最为考验玩家的逻辑推理能力,你不仅要具有当代福尔摩斯般敏锐的观察力和想象力,还要能勇敢的探索和决断。团队配合,将会是您胜出的关键! 理由3:体验过PC密室逃脱类游戏的玩家在体会找东西和解密快感的同时,还能体会到真实场景角色扮演的快乐。 理由4:各种梦幻场景,给玩家创造一个个惊喜。让玩家体会设计者的用心。 理由5:大型主题剧情机关解谜场景相结合,完美结合所有环节,更精致,更专业。

密室逃脱相关完整版

密室逃脱相关

Q1:别人做密室逃脱 从大二时,开始接触密室逃脱游戏,到今年寒假一天能玩上三、四次,“既然大家都喜欢,干嘛不自己也开一个呢?”黄皓月介绍,决定开一个“密室逃脱”店,很偶然,也很仓促。5个爱玩密室的女孩当即一拍即合,其中有两个女生已经工作,三个在校生中,黄皓月也即将本科毕业,她还“跨界”选修了金融专业的双学位。 三月份,考察江汉路所有密室逃脱店、玩遍40多个主题,敲定店址; 四月份,店面装修,自己设计主题、自己安装机关、自己制作装置……; “五一”,新店开张,首日客流量过百,不大的客厅里,挤满了前来“尝鲜”的同龄人。 开业的前一天,女孩们都在店里通宵加班,“创业比考研费精力多了,四月份的时候,天天熬夜。”黄皓月告诉记者,开店,比想象中的复杂、琐碎。对于“学霸”的称号,她觉得那是上高中以前的事了,从高二起,一直到现在,她更愿意把自己定义为“学霸班上的玩货”。 中考前,黄皓月因成绩优秀,提前保送到当地最好的学校——新洲一中“火箭班”。然而高一的第一次月考,成绩仅排在班上十几名,随后几次月考,更是滑落到二十几名。“我已经很努力了,学习成绩就是上不去。”家长的不理解,老师的不重视,让曾经的“神童公主”的骄傲光环瞬间破碎,从台上最闪亮的“明星”,跌落成台下普通观众,巨大的落差,让她一度“放松”自己,按照自己的意愿学习、生活,成为学霸班上的“另类”。 “其实二十几名已经很好了,至少可以考上武大华科,但那个时候没有人告诉我,我以为自己真的很差。”说到这里的时候,黄皓月的眼中有一丝伤感和落寞,虽然之后成绩时常徘徊在班上倒数几名,但高考总分依然超过一本分数线,考入湖北大学生物科学专业。 高中时期的那一段心路历程,对她触动很大,并且让“学霸班上的玩货”这一身份,一直保持到现在。今年,她们班上41名同学中,有三十几个考研,“没有同学相信我会考上,别人在学习的时候,我在玩。”她笑着说,作为前学生会副主席,如果她呆在寝室里安静的看书超过一周,所有人都会奇怪她没有不出去“活动”。 5月9日,小店开张后迎来的第一个双休日,女孩子们正在努力积累经验,希望在暑假旺季到来前,将自己打造成一个“全能女孩”。“我最大的优点,就是没有拖延症。”黄皓月觉得,“把今天做好”才是最重要的,至于最终结果是输是赢,只要自己努力过就好。她笑称,对未来没有明确的计划,如果读博的话,或许会考虑出国吧。 Q2采访5专家,2业主 业主1: 我是12年4月,5月的时候接触的密室逃脱,当时在武汉的EP逃逸层第一次接触到真人密室逃脱,当时整个武汉就两家,第一次体验了他们家的密室之后,我自己动了心思。6月我在创建了X-space真人密室逃脱。7月装修完开业,总共资金消耗6W不到,所有的谜题设计都是自己想的。那个时候也没什么房型啊,格局啊,什么的,一点纸片信息,一些文字内容,一点点的氛围,就用上房东家的家具,我们开业了。每天发发传单,做做问答,一个月的,收回了全部的成本还有不少的收益。当时也动了心思,想看看其他地方的密室逃脱是怎么做的,下一步,应该怎么发展。 永远要记住一点,你的视线永远要看着前方,比你能看到的位置更远的位置,然后收回来看脚下离目标有多远,选好节点,去执行。 我们的创业路径上很强调一个词,节点。后面也会不停的提到。

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