Human Behavior Modeling: The Necessity of Narrative

Roger Parker

## Abstract

As progress is made in the development of artificial intelligent mechanisms to assist human research into aspects of industrial, biomechanical and biomedical engineering, the conceptualization of mental behavior of human entities become more vital and more central to the success of any interaction between machine and humans. This discussion explores one of the most important features of human behavior, the fundamental and irreversible concept of narrative. The narrative is the essential construct for the theoretical understanding and presentation of human communication, including formal and informal logic, emotional wonder and desperation, noble and selfish biases, nationalism and globalist politics, and any form of spiritualism. This presentation offers a working definition of human narrative and proposes its basic structure that must be represented by any computer system which is required to deal with human behavior.

Keywords: human behavior modeling, narrative definition and structure, computer-human communications, logic modeling, bias modeling, emotional modeling, human agent-based modeling

### 1. Introduction

Artificial intelligence has made significant gains in operational context in the last two decades. One area of success has been demonstrated by the widespread implementation of agent-based modeling (ABM). Used extensively in a variety of contexts, there has been increased interest in the implementation of ABM's where the agents represent the behavior of individual or groups of human beings in a social context. This leads to the application of the irreversible concept of narrative [1]. The narrative is a fundamental concept for the theoretical understanding and presentation of virtually any kind of human behavior. Our discussion here presents an abstract, yet working, definition of the narrative. A basic structure that must be incorporated into any computer system which is required to deal with human behavior is offered, and the implications of that structure in a variety of human contexts are examined.

This approach to agent-based modeling has been in development for nearly two decades now. The first demonstration of the context in which a narrative became an essential part of a software system was created by AirMarkets Corporation, a collaboration of individuals from the air travel industry. It simulates the behavior of individuals and groups that have elected to fly from one city to another, including

when they are going to fly, how much each is going to pay, and how likely any particular flight option is [2].

of its current environment with its internal understanding of it and uses a causeand-effect chain to determine what it expects the future state of the environment will be if it undertakes some appropriate activity. The phrase cause-and-effect refers to an internal conception by the creature that the state of affairs perceived by the creature as currently the case is due, in large part, to relationships between things that occurred in the past and the current state, and the anticipated future state of affairs that will exist if the creature does some appropriate thing. It is this internal, mental construct that we call a narrative. Indeed, for the artificial intelligence purposes of this discussion, the definition of narrative that applies to the modeling of human agents is as follows: The term 'narrative' will be taken to mean a pattern maintained internally by an agent that represents temporal cause-and-effect chains which define events that are perceived by the agent, with which the agent 'understands' the events, and based on which the agent takes action. Since narratives are temporal cause and effect chains maintained in a mental structure, they are essentially inde-

Narratives are favorite topics of people who are interested in human creative activity, so much of the insight into their nature come from authors, poets, playwrights and composers. The term narratology is used to identify such discussions. Danto [5] defines the term atomic narrative to be a single event with related past and future. Figure 1 is an illustration of the concept. Note that there is past and future narrative time, and the event has a history and a range of outcomes.

This is the state space that obtained before the event occurs. To the right is an arc representing the set of possible outcomes of the event. An atomic narrative can have a set of discrete outcomes, a continuous range of outcomes, as shown in Figure 2, or, at least theoretically, a mixture of the two. The history here, of course, is history in narrative time, and compared with history in real time, is woefully incomplete. That is, there must exist states in real time history that are not in a narrative history, no matter how thorough the recording of that history might be. The actual content of the existence of reality is never completely known to humans because we are unable to completely absorb and understand a description of reality in a finite interval of

That there can be more than one possible outcome is a vital property of the event. An event occurs when something in reality changes because the agent recognizing and responding to the event promulgates the change. The change is actually realized as an event outcome. There is a family of probability distributions associated with the outcome set of every event. This family is a stochastic process indexed by a set of narrative variables referred to as resources and is thus called the event stochastic process. The stochastic process can be denoted PΛ(Y|X) which is the probability that outcome Y occurs given history X and resource allocation Λ. A molecular narrative is simply of a sequence (in time) of atomic narratives and a narrative can be made up of multiple molecular narratives that are simultaneous in narrative time. A narrative that contains such multiple molecular narratives is called

pendent of the reality they represent.

Human Behavior Modeling: The Necessity of Narrative DOI: http://dx.doi.org/10.5772/intechopen.86686

time.

Figure 1.

75

Schematic conceptualization of an atomic narrative.

The work supporting the initial formulation, resulted from extensive research by the author and his associates, culminating in the writing and publication of a doctoral thesis under the tutorage of global experts at the University of Technology in Sydney, Australia [3]. At that time artificial intelligence was still in its infancy, and the development of pattern recognition and the emergence of artificial intelligence technology based on such capability has since argued for the consideration of human-replacement computer systems. But artificial intelligence technology has a great deal of work to do before it can even come close to replacing ordinary humans, or even a broad class of animal life, in any kind of depth. This presentation gives a substantial indication of how far we have to go.

The next section of the discussion describes the narrative construct. The notion of event, time, and associated probability are essential concepts. We then incorporate these important narrative ideas used routinely in mathematical and computer models and simulation. These concepts are exemplified by a range of rational choice models, common to artificial intelligence computer development, but viewed from the perspective of human behavior modeling. This approach is illustrated briefly in the next section, which describes in overview form the AirMarkets Simulation, which is a computer program that represents the booking of over 40 million travelers on the world's commercial airline system using an agent-based, narrative structured methodology. We then move to a survey of heuristic choice model concepts, followed by an exploration of even more human—and less computer narrative structures, including social network protocols and sources of bias in human narrative execution.

We close with a review of future needed work, including mention of several mathematical theorems that limit the applicability of relevant software development regarding computer-based narrative software and distinctions between human and computer-based analysis that such analyses imply.

#### 2. A formal notion of narrative

The objective of this research is to develop computing machine ability to represent behavior in an agent-based software architectural context, so we can build agent-based models of activity such as market behavior, political contexts, or social interaction. Our first consideration is the creation of a framework for describing how human behavior can be characterized in a fashion consistent with observed patterns while dynamic enough to describe actions and activities that exist only, at least at some point in time, in the imagination of the individual. And time is an essential feature of such a characterization, since all behavior (of virtually all mobile biological entities) is selected, observed, enacted, realized, or even is cited as existing occurs over some distinct and evident time period. In addition, we want the description of the framework to be able to differentiate between what happens within the creature, such as choices based on memory or mental understanding, and external to the agent, such as weather and the behavior of other agents. That is, the construct must describe the relationship between what an agent perceives the world to be and what the world actually is.

Virtually all animal organisms show evidence of memory-based pattern creation and maintenance. In his fun discussion Montague [4] makes the case that virtually all biological entities that can move about has some ability to forecast at least the immediate future, and therefore must have at least a basic mental image of what the effect of its future activity is likely to be. The creature compares the perceived state Human Behavior Modeling: The Necessity of Narrative DOI: http://dx.doi.org/10.5772/intechopen.86686

when they are going to fly, how much each is going to pay, and how likely any

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

the author and his associates, culminating in the writing and publication of a doctoral thesis under the tutorage of global experts at the University of Technology in Sydney, Australia [3]. At that time artificial intelligence was still in its infancy, and the development of pattern recognition and the emergence of artificial intelligence technology based on such capability has since argued for the consideration of human-replacement computer systems. But artificial intelligence technology has a great deal of work to do before it can even come close to replacing ordinary

The work supporting the initial formulation, resulted from extensive research by

humans, or even a broad class of animal life, in any kind of depth. This presentation

We close with a review of future needed work, including mention of several mathematical theorems that limit the applicability of relevant software development regarding computer-based narrative software and distinctions between

The objective of this research is to develop computing machine ability to represent behavior in an agent-based software architectural context, so we can build agent-based models of activity such as market behavior, political contexts, or social interaction. Our first consideration is the creation of a framework for describing how human behavior can be characterized in a fashion consistent with observed patterns while dynamic enough to describe actions and activities that exist only, at least at some point in time, in the imagination of the individual. And time is an essential feature of such a characterization, since all behavior (of virtually all mobile biological entities) is selected, observed, enacted, realized, or even is cited as existing occurs over some distinct and evident time period. In addition, we want the description of the framework to be able to differentiate between what happens within the creature, such as choices based on memory or mental understanding, and external to the agent, such as weather and the behavior of other agents. That is, the construct must describe the relationship between what an agent perceives the world

Virtually all animal organisms show evidence of memory-based pattern creation and maintenance. In his fun discussion Montague [4] makes the case that virtually all biological entities that can move about has some ability to forecast at least the immediate future, and therefore must have at least a basic mental image of what the effect of its future activity is likely to be. The creature compares the perceived state

human and computer-based analysis that such analyses imply.

The next section of the discussion describes the narrative construct. The notion of event, time, and associated probability are essential concepts. We then incorporate these important narrative ideas used routinely in mathematical and computer models and simulation. These concepts are exemplified by a range of rational choice models, common to artificial intelligence computer development, but viewed from the perspective of human behavior modeling. This approach is illustrated briefly in the next section, which describes in overview form the AirMarkets Simulation, which is a computer program that represents the booking of over 40 million travelers on the world's commercial airline system using an agent-based, narrative structured methodology. We then move to a survey of heuristic choice model concepts, followed by an exploration of even more human—and less computer narrative structures, including social network protocols and sources of bias in

gives a substantial indication of how far we have to go.

particular flight option is [2].

human narrative execution.

2. A formal notion of narrative

to be and what the world actually is.

74

of its current environment with its internal understanding of it and uses a causeand-effect chain to determine what it expects the future state of the environment will be if it undertakes some appropriate activity. The phrase cause-and-effect refers to an internal conception by the creature that the state of affairs perceived by the creature as currently the case is due, in large part, to relationships between things that occurred in the past and the current state, and the anticipated future state of affairs that will exist if the creature does some appropriate thing. It is this internal, mental construct that we call a narrative. Indeed, for the artificial intelligence purposes of this discussion, the definition of narrative that applies to the modeling of human agents is as follows: The term 'narrative' will be taken to mean a pattern maintained internally by an agent that represents temporal cause-and-effect chains which define events that are perceived by the agent, with which the agent 'understands' the events, and based on which the agent takes action. Since narratives are temporal cause and effect chains maintained in a mental structure, they are essentially independent of the reality they represent.

Narratives are favorite topics of people who are interested in human creative activity, so much of the insight into their nature come from authors, poets, playwrights and composers. The term narratology is used to identify such discussions. Danto [5] defines the term atomic narrative to be a single event with related past and future. Figure 1 is an illustration of the concept. Note that there is past and future narrative time, and the event has a history and a range of outcomes.

This is the state space that obtained before the event occurs. To the right is an arc representing the set of possible outcomes of the event. An atomic narrative can have a set of discrete outcomes, a continuous range of outcomes, as shown in Figure 2, or, at least theoretically, a mixture of the two. The history here, of course, is history in narrative time, and compared with history in real time, is woefully incomplete. That is, there must exist states in real time history that are not in a narrative history, no matter how thorough the recording of that history might be. The actual content of the existence of reality is never completely known to humans because we are unable to completely absorb and understand a description of reality in a finite interval of time.

That there can be more than one possible outcome is a vital property of the event. An event occurs when something in reality changes because the agent recognizing and responding to the event promulgates the change. The change is actually realized as an event outcome. There is a family of probability distributions associated with the outcome set of every event. This family is a stochastic process indexed by a set of narrative variables referred to as resources and is thus called the event stochastic process. The stochastic process can be denoted PΛ(Y|X) which is the probability that outcome Y occurs given history X and resource allocation Λ. A molecular narrative is simply of a sequence (in time) of atomic narratives and a narrative can be made up of multiple molecular narratives that are simultaneous in narrative time. A narrative that contains such multiple molecular narratives is called

Figure 1. Schematic conceptualization of an atomic narrative.

#### Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

outcomes, but the distribution associated with the outcome of the last atomic narrative in a molecular narrative is not independent of the other atomic narratives which make up the molecular narrative. Therefore, every narrative has a past which contains the information required to support the future outcome stochastic

Human Behavior Modeling: The Necessity of Narrative DOI: http://dx.doi.org/10.5772/intechopen.86686

present and is conditioning the future. It has inaccuracies caused by imperfect recall and incomplete data. Memory deteriorates as events recede into the past, and hence there must be a probability associated with the accuracy of the recall, and hence the past of a narrative also has an associated stochastic process structure. This is illustrated in Figure 4. The past and the future are shown as the widening ends of a hyperbolic cone representing the increasing uncertainty surrounding the past and future of the narrative, and the present is the narrowest part of the hyperbolic surface. The small circles connected by the lines inside the hyperbolic solid represent the events of the narrative and their relationships, as perceived at that point in the narrative. These event conditions change from past to future, but the degree of uncertainty in the narrative increases as the past recedes and the looming distant future is contemplated. This results in the loss of resolution of the event description. The individual that creates and maintains a narrative is called its owner. All narratives have owners, and all narratives are unique, to a greater or lesser extent, to their owners. But narratives are also shared. Language and communications are important adaptive tools for the human species and a vital purpose served by language is the sharing of narratives. Indeed, some suggest [6] it is the sole purpose of language, since all communications in his view is the description of a narrative. Shared narratives are not, however, perfect copies held by each individual that is, part of the sharing group. Each individual owner at least modifies the shared narrative structure to fit their own unique set of compound narratives that include the shared one. Moreover, shared narratives become institutionalized into laws, codes of conduct, norms, and other social constructions that serve to assist in group adaptation and evolution. Shared narratives are a very powerful social and evolutionary tool. In fact, it could be argued that the interconnections of the atomic narratives that make up shared compound narratives may set the framework for the multilevel selection adaptation evidenced by group evolution. While beyond the

But the narrative past is not a completely accurate description of what lead to the

processes.

Figure 4.

77

The time structure of narratives.

Figure 3. Molecular narratives.

compound and is illustrated in Figure 3. As real time transpires, the course of the narrative is realized, which is illustrated in Figure 3 with the solid line connecting the relevant events.

Thus, narratives are memory structures that are retrieved under a currently perceived state configuration. The retrieval is not only representative of the present, but also reflects the relevant past and future. The future portion of the narrative is created from the expectation property created by the probability associated with the narrative. If one outcome results, the narrative will go one course, while if a different one results, it will follow a different path. The narrative carries with it a description of the expected consequences of these event outcomes. Thus, the description of the future consists of a set of branches, each opening a new course for the narrative to take. Furthermore, there is a probability distribution associated with each potential outcome which dictates a probability that the branch initiated by that outcome will be realized.

It should be noted that the stochastic structure of the probabilities cannot be assumed to be simple. The likelihood of outcome A vs. outcome B may depend on outcomes that were the result of previous events or sets of previous events. More succinctly, every atomic narrative has a probability distribution associated with its

#### Human Behavior Modeling: The Necessity of Narrative DOI: http://dx.doi.org/10.5772/intechopen.86686

outcomes, but the distribution associated with the outcome of the last atomic narrative in a molecular narrative is not independent of the other atomic narratives which make up the molecular narrative. Therefore, every narrative has a past which contains the information required to support the future outcome stochastic processes.

But the narrative past is not a completely accurate description of what lead to the present and is conditioning the future. It has inaccuracies caused by imperfect recall and incomplete data. Memory deteriorates as events recede into the past, and hence there must be a probability associated with the accuracy of the recall, and hence the past of a narrative also has an associated stochastic process structure. This is illustrated in Figure 4. The past and the future are shown as the widening ends of a hyperbolic cone representing the increasing uncertainty surrounding the past and future of the narrative, and the present is the narrowest part of the hyperbolic surface. The small circles connected by the lines inside the hyperbolic solid represent the events of the narrative and their relationships, as perceived at that point in the narrative. These event conditions change from past to future, but the degree of uncertainty in the narrative increases as the past recedes and the looming distant future is contemplated. This results in the loss of resolution of the event description.

The individual that creates and maintains a narrative is called its owner. All narratives have owners, and all narratives are unique, to a greater or lesser extent, to their owners. But narratives are also shared. Language and communications are important adaptive tools for the human species and a vital purpose served by language is the sharing of narratives. Indeed, some suggest [6] it is the sole purpose of language, since all communications in his view is the description of a narrative. Shared narratives are not, however, perfect copies held by each individual that is, part of the sharing group. Each individual owner at least modifies the shared narrative structure to fit their own unique set of compound narratives that include the shared one. Moreover, shared narratives become institutionalized into laws, codes of conduct, norms, and other social constructions that serve to assist in group adaptation and evolution. Shared narratives are a very powerful social and evolutionary tool. In fact, it could be argued that the interconnections of the atomic narratives that make up shared compound narratives may set the framework for the multilevel selection adaptation evidenced by group evolution. While beyond the

Figure 4. The time structure of narratives.

compound and is illustrated in Figure 3. As real time transpires, the course of the narrative is realized, which is illustrated in Figure 3 with the solid line connecting

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

Thus, narratives are memory structures that are retrieved under a currently perceived state configuration. The retrieval is not only representative of the present, but also reflects the relevant past and future. The future portion of the narrative is created from the expectation property created by the probability associated with the narrative. If one outcome results, the narrative will go one course, while if a different one results, it will follow a different path. The narrative carries with it a description of the expected consequences of these event outcomes. Thus, the description of the future consists of a set of branches, each opening a new course for the narrative to take. Furthermore, there is a probability distribution associated with each potential outcome which dictates a probability that the branch initiated

It should be noted that the stochastic structure of the probabilities cannot be assumed to be simple. The likelihood of outcome A vs. outcome B may depend on outcomes that were the result of previous events or sets of previous events. More succinctly, every atomic narrative has a probability distribution associated with its

the relevant events.

Molecular narratives.

Figure 3.

76

Figure 2.

Discrete and continuous outcome sets.

by that outcome will be realized.

scope of this discussion, future research in this area using computer-based agents designed with the narrative construct might yield very useful results. It also should be noted that other modes of communications aside from formal speech are also available both to humans and to other species. Indeed, the question arises as to whether or not there is some form of communications available to plants?

intersubjective verifiability of narrative content [9]. Indeed, everyone has experienced erratic and unpredictable behavior on the part of others. Such behavior is seen because the narrative driving the erratic individual is different in some important way from what the observer expects, given her constellation of narratives. Since narratives exist as neurological entities (fundamentally memories), study of their physical existence and attendant properties resides in the domain of the neuroscientist. But what is represented by the narrative pattern, and whether or not that has any reality outside of the narrative itself, is an entirely separate question. It can be as fanciful or factual as the owner wishes and is capable of managing. The fact that fanciful narratives can be as real as factual ones creates the conditions on which wonderful fiction can be portrayed, both in books, on television, and in the theatre. It also creates the capability for popular politicians and dangerous authoritarians to put forth absurd and violent behaviors as within human and normal perspectives. Simply review the history of political activity in Europe prior to

Human Behavior Modeling: The Necessity of Narrative DOI: http://dx.doi.org/10.5772/intechopen.86686

With respect to building a computer program, there is a minimal organization

Corresponding to the memory for a human being, the state vector is an array of variables which describe the current state of the agent. Referring to this array as a vector does not imply that it is necessarily a precise set of real numbers laid out in a row or column manner. Structures more complex than simple numbers can be specified. However, it does imply the memory object is both precisely well-defined and finite. The state vector also encodes the appropriate narrative structures that represent the beliefs and aspirations of the agent being considered. For example, it contains the probability distributions of the outcome sets relevant to the events the agent will encounter during the simulation. The state vector is maintained as

required for the implementation of a narrative in the context of agent-based modeling. The general definition of agent has been presented in Parker and Bakken [10] and in Parker and Perroud [11]. For our purposes, agents will always be implemented in the form of computer programs. Within the agent are mechanisms for perceiving the environment, making choices about what to do given the environmental information and from the context of the agent's motivating narrative, and taking actions that advance the agent's narrative. This simple agent structure

consists of four components and is illustrated in Figure 5.

appropriately defined data structure.

World War II.

Figure 5.

79

The general structure of an agent.

It is reasonable to conclude that narratives are mechanisms that result from human evolution. Much like the arguments presented by Sober and Wilson [7] and Shermer [8], the narrative hypothesis can be reinforced by evolutionary feasibility and their proven success as an adaptive device in support of both individual and group survival. This leads to the inference that the outcome of a narrative is either desirable or undesirable. Some are very simple, like what to have for lunch. At the other extreme, compound narratives that support religious beliefs and institutions can be invoked to portray a future where death is merely a change in physical state from this world to another, which may be Heaven (desirable), Purgatory (not so well favored) or Hell (clearly undesirable). Given this property of desirability, narratives then can be considered to be the vehicle by which value is expressed by the individual, and it is from narratives that values arise as identifiable attributes of human behavior.

The narrative framework can be considered as a storage device for not only memories, but also expectations. The role of expectation in narrative structure—the projection of the effect associated with some cause in the future—is quite clear. The maintenance of a set of expectations associated with the various outcomes in a particular event-based choice situation is required for the mental storage of such expectations. It is implying too much inherent human analytic ability to assert that each such expectation set has a formal probability distribution also maintained as part of its mental representation, especially since such distributions would be dependent on the pathway by which the event itself arose, and not only on the fact that the event came about at all. Nonetheless, narratives provide both a context and framework within which expectation can be stored, recalled and manipulated. However, for the purposes of modeling human agents, the expectation-storage property is important. Indeed, one of the most useful applications of an agent model might be that both the "correct (logical)" application of probabilities, or the "deficient (logically false but commonly held)," expectation distributions can be modeled, and the results as expressed in specific choice contexts compared.

A human individual is part of a world and is in constant interaction with it. The intervention in the real world by the agent occurs by means of the allocation resources to the event at hand in order to alter its perceived probability distribution of the outcomes, either toward those it favors, or away from those it dislikes. In this way, the values held in the narrative are expressed in action. Since compound narratives are molecular, and thus made up of sequences of atomic narratives, any attempt by an agent to affect what it thinks will be the desired course of a narrative involves choosing a specific outcome from the set of outcomes of the narrative representation of that event. And it must do so one event at a time. Otherwise contradictions, uncontrolled feedback, process deadlock or other dangerous anomalies would emerge. Thus, in the narrative framework, the relationship between the narrative owner and its environment is always an event, and therefore always a choice problem. And there are a number of other issues of interest, but beyond the scope of this discussion. What about the time it takes to select outcomes? What happens during that selection process? And what about the cost of being wrong? Or how does the narrative accommodate the shifting environment.

Notice there is absolutely no requirement that any of the events or sequences of events described by a narrative be true, in the sense that there is Karl Popper's

#### Human Behavior Modeling: The Necessity of Narrative DOI: http://dx.doi.org/10.5772/intechopen.86686

scope of this discussion, future research in this area using computer-based agents designed with the narrative construct might yield very useful results. It also should be noted that other modes of communications aside from formal speech are also available both to humans and to other species. Indeed, the question arises as to whether or not there is some form of communications available to plants?

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

It is reasonable to conclude that narratives are mechanisms that result from human evolution. Much like the arguments presented by Sober and Wilson [7] and Shermer [8], the narrative hypothesis can be reinforced by evolutionary feasibility and their proven success as an adaptive device in support of both individual and group survival. This leads to the inference that the outcome of a narrative is either desirable or undesirable. Some are very simple, like what to have for lunch. At the other extreme, compound narratives that support religious beliefs and institutions can be invoked to portray a future where death is merely a change in physical state from this world to another, which may be Heaven (desirable), Purgatory (not so well favored) or Hell (clearly undesirable). Given this property of desirability, narratives then can be considered to be the vehicle by which value is expressed by the individual, and it is from narratives that values arise as identifiable attributes of

The narrative framework can be considered as a storage device for not only memories, but also expectations. The role of expectation in narrative structure—the projection of the effect associated with some cause in the future—is quite clear. The maintenance of a set of expectations associated with the various outcomes in a particular event-based choice situation is required for the mental storage of such expectations. It is implying too much inherent human analytic ability to assert that each such expectation set has a formal probability distribution also maintained as part of its mental representation, especially since such distributions would be dependent on the pathway by which the event itself arose, and not only on the fact that the event came about at all. Nonetheless, narratives provide both a context and framework within which expectation can be stored, recalled and manipulated. However, for the purposes of modeling human agents, the expectation-storage property is important. Indeed, one of the most useful applications of an agent model might be that both the "correct (logical)" application of probabilities, or the "deficient (logically false but commonly held)," expectation distributions can be modeled, and the results as expressed in specific choice contexts compared.

A human individual is part of a world and is in constant interaction with it. The

Notice there is absolutely no requirement that any of the events or sequences of

events described by a narrative be true, in the sense that there is Karl Popper's

intervention in the real world by the agent occurs by means of the allocation resources to the event at hand in order to alter its perceived probability distribution of the outcomes, either toward those it favors, or away from those it dislikes. In this way, the values held in the narrative are expressed in action. Since compound narratives are molecular, and thus made up of sequences of atomic narratives, any attempt by an agent to affect what it thinks will be the desired course of a narrative involves choosing a specific outcome from the set of outcomes of the narrative representation of that event. And it must do so one event at a time. Otherwise contradictions, uncontrolled feedback, process deadlock or other dangerous anomalies would emerge. Thus, in the narrative framework, the relationship between the narrative owner and its environment is always an event, and therefore always a choice problem. And there are a number of other issues of interest, but beyond the scope of this discussion. What about the time it takes to select outcomes? What happens during that selection process? And what about the cost of being wrong? Or how

does the narrative accommodate the shifting environment.

human behavior.

78

intersubjective verifiability of narrative content [9]. Indeed, everyone has experienced erratic and unpredictable behavior on the part of others. Such behavior is seen because the narrative driving the erratic individual is different in some important way from what the observer expects, given her constellation of narratives. Since narratives exist as neurological entities (fundamentally memories), study of their physical existence and attendant properties resides in the domain of the neuroscientist. But what is represented by the narrative pattern, and whether or not that has any reality outside of the narrative itself, is an entirely separate question. It can be as fanciful or factual as the owner wishes and is capable of managing. The fact that fanciful narratives can be as real as factual ones creates the conditions on which wonderful fiction can be portrayed, both in books, on television, and in the theatre. It also creates the capability for popular politicians and dangerous authoritarians to put forth absurd and violent behaviors as within human and normal perspectives. Simply review the history of political activity in Europe prior to World War II.

With respect to building a computer program, there is a minimal organization required for the implementation of a narrative in the context of agent-based modeling. The general definition of agent has been presented in Parker and Bakken [10] and in Parker and Perroud [11]. For our purposes, agents will always be implemented in the form of computer programs. Within the agent are mechanisms for perceiving the environment, making choices about what to do given the environmental information and from the context of the agent's motivating narrative, and taking actions that advance the agent's narrative. This simple agent structure consists of four components and is illustrated in Figure 5.

Corresponding to the memory for a human being, the state vector is an array of variables which describe the current state of the agent. Referring to this array as a vector does not imply that it is necessarily a precise set of real numbers laid out in a row or column manner. Structures more complex than simple numbers can be specified. However, it does imply the memory object is both precisely well-defined and finite. The state vector also encodes the appropriate narrative structures that represent the beliefs and aspirations of the agent being considered. For example, it contains the probability distributions of the outcome sets relevant to the events the agent will encounter during the simulation. The state vector is maintained as appropriately defined data structure.

Figure 5. The general structure of an agent.

Like the ability of humans to receive, filter and understand information, a computer agent can receive information about the current conditions in its environment through the code component called the perceptor. Almost without exception, perceptors are message-handling routines the purpose of which is to 'observe' the current state of the agent's environment, and filter and translate that information into a form that can be used by the choice-making component of the agent, converting the messages from the environment into an internal form of use to the agent. This internal translation is unique to each agent, which thus allows for agents that interpret the same external message differently. This would be important if different agents have different narrative events that were triggered by the same external environmental conditions.

3. Modeling and simulation

Human Behavior Modeling: The Necessity of Narrative DOI: http://dx.doi.org/10.5772/intechopen.86686

problem at hand.

sort of being simulated.

explicitly studied.

81

ecological dynamics and evolutionary economics.

Human beings think in terms of mental models of the world around them and

We are only concerned with models that are represented mathematically, either in the form of one or more equations or as a computer program. Consider the simple "What if?" scenario analysis applied to the results from a conjoint analysis, or more elaborate "war games" in which competing teams of managers (or MBA students) develop and implement strategies in an interactive fashion. An underlying principle of modeling is the representation of one process or set of processes with a simpler process or set of processes. The Monopoly® board game, for example, reduces a complex economic system into a limited set of transactions. Part of the fun of playing the game derives from the degree to which the outcomes (wealth accumulation and bankruptcy) resemble the outcomes from a real-world process that is,

An important concept regarding simulation is time. This temporal property is an essential characteristic of process-representative simulation and differentiates the application of simulation as an analytic and scientific tool from many other

approaches, such as deductive logic or statistical inference. As noted in the previous section, the central role of time is also an integral part of agent-based model simulations. With an express representation of time, the dynamics of a system can be

In many circumstances, the phenomenon under investigation cannot be ethically or safely subjected to experimentation. The study of disease epidemics, social intolerance and military tactics are obvious examples. In many situations the scale of the process under study prohibits any other approach. In astronomy and astrophysics, the universe is not available for experimental manipulation, but a simulation of important aspects of it are available for such study. Similarly, explorations of cultural development or species evolution cannot be executed with physical laboratory environment, while a simulation permits hypothesis testing and inference on a reasonable time scale. Finally, some systems are so complex that traditional experimental science seems hopeless as a research approach. Among these systems are

Figure 6 lays out the "ethnology" of mathematical modeling in general and simulation modeling in particular, starting with the invention of the calculus in the 1700s and continuing up to the present day. (This diagram is adapted from Gilbert and Troitszch [13].) The two broad categories of stochastic and deterministic simulation models are indicated by the shaded ovals. The bold face labels define the mathematical contexts of the various modeling formalizations, while their genealogy is spelled out with the lines. As illustrated in this diagram, agent-based simulation belongs to the class of stochastic simulations and descended from a form of simulation called discrete-event simulations. Discrete event simulations are stochastic simulations that attempt to mimic the behavior of the discrete parts of a system and

their relationship to it [12]. We formulate concepts and ideas and link them together to represent the way we think the world works in some significant regard and use those representations to make decisions on our future actions. These models can range from simple statements of assumed cause and effect—"if I step out in front of a moving bus, there's a good chance I could be seriously hurt"—through physical scale models of buildings or vehicles in their design stages through mathematical representations of complex social or physical systems. Common to models of whatever composition or subject is that they are abstractions, and therefore simplifications, of reality, retaining what is believed to be salient features of the

The actions of the agents to external messages, both those which are put into the environment as output messages by other agents, those required for changes in the internal state vector of the agent, and those that require the attention of other agents, are managed by the ratiocinator component. The ratiocinator makes choices and adapts the behavior of the agent. This is the part of the agent which replicates how the human intersects with the simulated world in accordance with the thenactive narrative structure. How such stochastic choice mechanisms are implemented are within the purview of the agent's ratiocinator.

When it is required that an agent create input to other components of the agentbased simulation, it can issue messages out to the environment by way of what we call an actor. It specifically engages the agent environment. Messages intended for other agents are detected by those agents as they interact with the environment.

For even the simplest case of the atomic narrative with a single event, the agent components must contain significant data. The perceptor needs to be designed to recognize the occurrence of the event and the perceived state of the environment at the time of the occurrence. The ratiocinator must be programmed to perform one of a set of choice protocols the agent will apply to exercise the choice required by the event, including the availability and allocation conditions of the resources at the agent's disposal. And the actor must have, in its repertoire of possible actions, those that are suitable given the event perception and protocol requirements. If the narrative construct driving the event recognition and intervention is a molecular narrative, with a number of events connected in a time-ordered and contingency network, then each atomic narrative must be delineated as described above. Furthermore, the connections between the component events must also be precisely represented in order to reliably represent the agent's actions.

This definition of agent contains the basic outline of all the pieces of the programmer's art necessary to build and execute a virtual market simulation. At first glance, these requirements may seem onerous. But in cases where the construct has been applied, the problem reduces itself to a tractable, if perhaps complex, computer algorithm programming task. As with all computing programs, two elements are present: the data on which the algorithms operate and the computing code which executes the algorithms themselves. Looking at the agent definition from that perspective, the state vector holds the data, and the perceptor, ratiocinator and actor consist of algorithms. From experience to date, and from reflection of how a particular agent behavior might be implemented in a variety of other contexts, the programming of the choice protocol set that resides in the ratiocinator seems the most daunting.

Why do care in the least about narratives? Because they represent—in fact are the existential structure—of what us advanced species of humans refer to as models. Now let us look at that!
