Communication model elements for societal behavior representation using agent based models
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Communication model elements for societal behavior representation using agent based models. Charles D. Turnitsa GEMS Institute Columbus State University Columbus, Georgia. Presentation Overview. Introduction Societal Behavior Representation Model Communication Models Model Representation

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Communication model elements for societal behavior representation using agent based models

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Communication model elements for societal behavior representation using agent based models

Communication model elements for societal behavior representation using agent based models

Charles D. Turnitsa

GEMS Institute

Columbus State University

Columbus, Georgia

Presentation overview

Presentation Overview

  • Introduction

  • Societal Behavior Representation Model

  • Communication Models

  • Model Representation

  • Future Work

  • Bibliography


Section 1


Who what when where

Who What When Where

  • Charles Turnitsa

    • Assistant Professor for Computer Science (Modeling & Simulation)

    • Director GEMS Institute (Gaming, Education, Modeling & Simulation)

  • Three Student Research-Assistants

    • Hugh Kwon

    • Cedric Searcy

    • Ian Blake-Knox

  • Work follows up on earlier efforts

    • HSCB work done at ODU/VMASC, and through GDIT for US DOD

    • Research work done at 2011/2012 NEH Summer Institute

    • Earlier Efforts presented at SIW, SpringSim, and WinterSim

    • Students will be presenting their efforts at ACM Mid-Southeast 2013

Societal behavior modeling

Section 2

Societal behavior modeling

Hbr at societal level

HBR at Societal Level

  • Beginning premise: the existence of a group motivator for behavior within a human society is a complex system. Representing it could, if possible, best be done with a model based simulation.

  • Complex System – The system consists of many individual transactions, which can be represented, but the interconnectedness of those transactions, means that the resulting system is complex.

  • Situation Theory can be relied on to allow the capture points in time of the overall system. This is accomplished, in theory, by representing the state of all the component parts of the whole system at a point in time.

  • Understanding how the overall aggregation of those states move forward or backward through time does not appear to be reducible to mathematical representation. Simulation, however, may provide some insight.

Hbr assumptions

HBR Assumptions

  • Behaviors

    • Behaviors are driven by some motivation.

    • Motivations are driven by beliefs, beliefs can be changed based on observation or persuasion.

  • Societies

    • Societies are made up of individuals.

    • If a society has a behavior, it is driven by the motivations of the members of that society

    • Being part of the society may affect the motivations of the individuals.

  • Behavior Representation

    • In order to represent behaviors, and their change, the motivators must be represented

    • Internal representation of a behaving agent contains beliefs, and a calculus that transforms those beliefs into motivation

    • A reinforcement structure where internal beliefs affect and support each other, making them more or less resilient to the various calculi of change

    • External representation of transactions between agents should represent:

      • Observation of other behaviors, a calculus that transforms those observations into Belief Changes

      • Persuasion interactions between agents, and a calculus that transforms those attempts into Belief Changes

Multi resolution hbr

Multi Resolution HBR

  • An earlier SISO presentation (10F-SIW-063, see bibliography for reference)) presented a proposal for accomplishing multi-resolution Human Behavior modeling

  • The idea was to rely on what Kurt Lewin (1938, 1945 see bibliography for references) referred to as “psychological forces” – the aggregation of what we are here referring to as motivators, based on beliefs.

  • The proposed idea was that if the attributes of a behavior (requiring the attributes mentioned on the previous slide) could be gathered, then the behavior of a group of individuals could be presented in aggregate, by treating the behavior attributes as vectors

Models based on psychological forces

Models based on Psychological Forces

  • In a very much simplified form, this theory is based on the identification of psychological forces, and the reduction of those to representable variables.

  • To project this idea to a group representation, then the individual's competing forces must be reduced to showing a single behavioral thrust – represented as a vector

  • The sum of individuals to determine the behavior of the group is then undertaken (Caution: several dangers exist)

  • In order to effectively use this for HSCB modeling, a number of different research objectives must be addressed.


Individual modeling

Individual Modeling

  • In order to get the overall measure of societal behavior, the proposed aggregation method supposes that individual modeling can be done

  • In particular, given our assumptions about behaviors and motivators, this amounts to two items:

    • Modeling the observations an agent makes of activities, and the calculus that transforms this into changes of beliefs

    • Modeling the communications between agents, when they are attempts to persuade, and the calculus that transforms this into changes of beliefs

  • Based on interests of my research assistants, we decided to tackle the communications problem first – this is the main focus of the paper

Communication models

Section 3

Communication models

Approach to modeling communications

Approach to modeling Communications

  • Assumptions

    • The group at Columbus State University is targeting agent based models as the method of instantiating these models

    • By understanding the history of how communications has been thought of in abstract (modeled) it should be possible to identify the elements required by an M&S modeler

    • The specific elements and participants of the communications model should be made as explicit as possible, to help researchers identify which elements they need for their own effort

  • General Approach

    • Our approach serves the ongoing HBR research program mentioned earlier

    • Wanted it to be a general approach useful to others representing Communications in an agent based model

    • Communication Models addressed in five successive models

  • Role of Communication Elements

    • The identified elements of a communications model have been given a role based on the OPR Framework of conceptual model elements.

    • Each element is either an Object, Process or Relation

Object process relation framework

Object, Process Relation Framework

  • The conventions of the OPR framework (Turnitsa 2013, reference in bibliography) are being followed here, identifying the components of each model as one of the following:

    • Object (something that retains identity, and state, unless acted on by another component), defining parameters are called Attributes

    • Process (also retains identity and state, but is responsible for producing some effect on one or more components), defining parameters are called Characteristics

    • Relation (also retains identity and state, but is responsible for associating together two or more components), defining parameters are called Rules

  • By relying on this neutral framework, the basic functioning of each component in the communications model can be described, but without relying on either undefined words or terms from a particular modeling technique.

Early models 1

Early Models (1)

  • It could be argued that the earliest communications models come from Aristotle, in two of his works, De Interpretatione, and Rhetoric

  • Initially the idea is that communication consists of three elements –

    • The Speaker constructs a Message, and relays it to the Listener.

  • This idea was modified in Rhetoric to include the following concepts:

    • Four Phases of the communications act

    • The Speaker becomes aware of some Proof (one or more) of a Notion (or belief) that they hold

    • The Speaker composes their Proofs into a Strategic Arrangement.

    • The Speaker uses Words to transfer their Strategically Arranged Proofs into a compelling Message

    • The Speaker relays their message to the Listener.

  • Symbology – the models here will rely on the symbology introduced by Searle (1975, discussed in the paper) to represent a locutionary act – F(p) where F() is the attempt to transfer the information, p. This approach is followed throughout.

Early models 2

Early Models (2)

  • Aristotle’s model held sway for a long time, and for many cases today it is still quite useful

  • There are at least two assumptions

    • First, that the Listener will be able to understand the words that are used to convey the Message

    • Second, that the Listener will make the same connection between those words and the Arrangement of Proofs that they represent\

  • The assumption that the Words and their underlying Meaning can only have one interpretation was the cause of a split amongst Medieval Scholars

  • Those that thought that there is one, knowable, apparent nature of things were Realists, and those that believed that there is a possibility to assign meaning to a word, and that therefore the meaning of things is dynamic to a certain extent, were known as Nominalists

Communications model f 0

Communications Model F0

  • In order to take Aristotle’s view and model it, some assumptions must be added to those already listed

  • First, in order to get from a Speaker to a Message, and from a Message to a Listener, there are required at least two processes

  • Second, the transfer of the produced Message from Speaker to Listener is also a process

  • Because of these assumptions, some components are Explicit, and some are Implicit

Communications model f 1

Communications Model F1

  • Shown by (Gronbeck, et al, 1988) that F0 is implying several cognitive stages (the assembling of thoughts, the construction of statement, the interpretation of statement, etc)

  • Following the terminology introduced there, the idea becomes the Thesis, which is then formulated into a Statement

  • In order to show that the purpose is to convince, the model introduces as the source and target, the Speaker’s and Listener’s cognitive state

  • The Formulation of the Thesis, and the Production of the Statement are considered separate processes, as they may be treated separately

Communications model f 1 continued

Communications Model F1 (continued)

Models based on symbol and meaning 1

Models based on Symbol and Meaning (1)

  • In order to tackle the issue of Thesis and Statement production it is useful in the history of communications models to turn to the ideas of semantics (meaning) and semiotics (symbols)

  • One version of the connection between meaning and symbols that has been used in a variety of different papers presented as Simulation Interoperability Workshops is the Triangle of Meaning, as presented by Ogden and Richards (1923). The idea existed prior to that reference, and has since been refined, but was illustrated and explained in the paper because of its prevalence in SIW papers.

Models based on symbol and meaning 2

Models based on Symbol and Meaning (2)

  • What is being conveyed p by the act of communication F() is a symbol, that symbolizes the strategically assembled proof(s), that in their cognitive state, represents the referent idea

  • Each of these steps make up a part of assembling a symbol (message) for transfer, so could be potentially part of a model

  • Concept Adequateness and Symbol Correctness can be calculated based on the understanding of the listener

  • That the listener can created the linkage between Symbol, back to Thought, and eventually to Referent is a measure of the listener’s understanding, or perception of what is being communicated

Communications model f 2

Communications Model F2

Models based on information transfer 1

Models based on Information Transfer (1)

  • The beginnings of modern communications modeling, it can be argued, stem from Shannon and Weaver (1949, reference in bibliography)

  • They were considering communications, for the first time, as something that takes place with the use of created devices – in their case, electronic communications (such as a telephone line)

  • New view allows consideration of the medium through with the message travels, as well as the sending and receiving a message into and out of that medium

  • F(p) takes on several new features here . . .

  • Most notably the medium of communication is now referred to as the Channel, and the acts of preparing a message for the channel, and retrieving it from the channel are referred to as Encoding and Decoding

Communication model elements for societal behavior representation using agent based models

  • Information Source produces message

  • Message is encoded to a signal

  • Signal traverses a channel

  • Receiver decodes signal back into message

  • Destination consumes message

Basic communications model (Shannon and Weaver 1949) showing basic steps between source and destination, including encoding, decoding, and channel traversal













Gulf between source and destination may include sources of noise to be introduced to the channel

Models based on information transfer 2

Models based on Information Transfer (2)

  • The basic stages from Shannon and Weaver were simplified into stages by Berlo (1960), those being Source, Message, Channel and Receiver (SMCR)

  • Berlo’s SMCR model is a modification of Shannon and Weaver, but there are introduced some helpful attributes of the various objects (source, message, channel and receiver).

Models based on information transfer 3

Models based on Information Transfer (3)

  • Missing, of course, are the cognitive elements from F2 and also the fact that the whole approach is based on modeling the act of communications from the perspective of the Speaker.

  • What became possible, however, by adopting Shannon and Weaver’s view is the ability to model and represent some specific elements of the information being transferred. This is the beginning of the study of information theory, and the new features include (among others):

    • Entropy – the amount of uncertainty in a system

    • Redundancy – the amount of non-unique (or repeated) information within a system

    • Noise – additional information received by the receiver that was not originated by the transmitter

    • Channel Capacity – how much information the channel can convey in a certain amount of time.

Communications model f 3

Communications Model F3

Communications model f 3 continued

Communications Model F3 (continued)

Models based on social interaction 1

Models based on Social Interaction (1)

  • Communications is a social interaction

  • This means several things

    • It is two way (each participating agent is both in the role of Speaker and Listener)

    • The act of communicating, itself, may have meaning (why and how carry their own meaning)

    • The internal structure of referent/concept/symbol is changing based on ongoing communication

  • These have each been addressed by different research, and can be included in our (for now) last model, F4

Models based on social interaction 2

Models based on Social Interaction (2)

  • Each participating agent is in the role of speaker, and of listener, at different (or perhaps the same) time. This has been addressed very well by Schramm (1955) and is illustrated on the following slide.

  • The implication in this is that each agent, in a model that is to represent this, will have to have a role indicator (or switch) and be equipped to access all processes that would be available to either the speaker or listener (as well as have all associated objects and defining parameters for those components)

  • Schramm also addresses the communications from the perspective of the listening role – and generalizes production, with the receiving end version of the same, and calls it “interpretation” – meaning the traversal of the Triangle of Meaning, either forwards (to produce a symbol) or backwards (to understand a symbol)

Communication model elements for societal behavior representation using agent based models







Ongoing Two-Way Communication, both Agents interacting

Models based on social interaction 3

Models based on Social Interaction (3)

  • When you choose to say something; How you choose to say it; Why you choose to say what you do – these all convey information, as much as, or perhaps in some instances, more than what it is you are actually saying.

  • Grice (1975) calls these elements being conveyed Implicatures. Such information is based on the fact that two communicating agents have some set of rules – maxims – guiding the acts of communication.

  • Following these will give the expected accurate presentation of information – deviating will convey other information. Here is an incomplete list of the Maxims:

Models based on social interaction 4

Models based on Social Interaction (4)

  • Maxim of quantity - The maxim of quantity applies when a statement concerning a quantitative piece of information should also have implied that the quantity is not only truthful but also conveys some additional information. An example is that if it is stated that “I have 3 gallons of gas”. That is true if I have 3 gallons, and it is also true if I have 6 gallons. However if my statement is a response to a query about how much fuel I have left, if I say I have 3 gallons when I have 6 gallons, I am implying that I ONLY have 3 gallons. I have conveyed less useful information

  • Maxim of quality - The maxim of quality applies when you may make a statement that is on the surface true, but you know that it is false. For example if you say “The scouts have reported seeing the enemy at the Bridge.” This statement is true, if the scouts have indeed made such a report. But if you know that the scout report was wrong, then you imply different information when you make the statement.

  • Maxim of relation - The maxim of relation applies when you make a statement that is itself true, but is not relevant to the exchange of statements between two communicators. For instance, if (as in the previous example), there had been several exchanged communications concerning the Red bridge, and then you make the statement “The scouts have reported seeing the enemy at the Bridge.” If you are being relevant, then this will be concerning the Red bridge, which can be implied. The statement may be, instead about the Blue bridge, and it is true, but the relevance dictated by context means that you have conveyed false information.

  • Maxim of manner - The maxim of manner applies to avoiding obscurity and ambiguity. The example concerning fuel, where a statement is made in response to a question, may have a speaker make the statement “I have several gallons of fuel.” That may be true, and it may imply enough fuel, but in reality it may not be enough. It is far better to be brief and orderly, and to also follow the other maxims, so that what is stated is in alignment with what is implied.

Communications model f 4

Communications Model F4

Communications model f 4 continued

Communications Model F4 (Continued)

Communications model f 4 continued1

Communications Model F4 (Continued)

Communications model f 4 continued2

Communications Model F4 (Continued)

Model representation

Section 4

Model Representation

Agent based modeling

Agent Based Modeling

  • There are numerous efforts at modeling communications within ABMs

  • Applying this framework (of models F0 through F4, or some appropriate subset) could represent a time savings for the researcher who has yet to select a communications model

  • Additional elements can be thought of to include, then, based on the individual researcher’s requirement to extend the framework

  • Example: In Abrams (2013) work based on Thagard (2000), there is an effort to represent internal relationships between beliefs, and to model the perceived and actual threats to the internal reinforcement by using related arguments in a communication, that are designed not to change a belief, but to remove the supporting beliefs (softening the target, if you like) – this could easily be seen as an extension to F4



  • I have two students currently working on implementations of these models, from two different perspectives

  • Both are using Netlogo

  • Both will be presented at a future conference (BRIMS, SIW, ??)

  • Both are planning to have preliminary presentations at ACM Mid-Southeast 2013

  • Difference – one is modeling where external factors can be adjusted (such as medium, distance of communicating parties, etc; Other is modeling where internal factors can be adjusted

Future work

Section 6

Future work

Engagement with cognitive science

Engagement with Cognitive Science

  • The Columbus State University group is reaching a point where further work will require further understanding of the cognitive theory behind the models

  • Engagement with a Cognitive Scientist (communications expert, linguistics expert, social interactions expert, etc) is something that we are looking for. If you Are One or if you Know One – please let me know.

Re engagement with social behavior model

Re-engagement with Social Behavior Model

  • Taking these individual communication models and then reintegrating them with the Social Behavior model is a future step

  • Representing the internal cognitive state of an agent as the Motivator Vector for that individual is one way of accomplishing this

  • Aggregation of the Motivator Vectors will be the next step

  • Identifying and researching the possibility of something like Group Implicatures – that affect understanding (and cognitive state) based on what activities take place within a group, and the composition of that group, will be the next step


Section 6


Works cited 1

Works Cited (1)

  • Abrams, M. (2013). A Moderate Role for Cognitive Models in Agent-Based Modeling of Cultural Change. Forthcoming in Special Issue on Modeling Large Scale Communication Networks using Complex Networks & Agent-based Modeling Techniques, in Complex Adaptive Systems Modeling.

  • Alam S.J., Geller A., Meyer R., Werth B. (2010). Modelling Contextualized Reasoning in Complex Societies with “Endorsements". Journal of Artificial Societies and Social Simulation 2010, 13(4):6,

  • Austin, J.L. (1975). How to Do Things with Words. Oxford: Oxford University Press.

  • Berlo, D. K. (1960). The process of communication. New York, New York: Holt, Rinehart, & Winston.

  • Borg, E. (2004). “Formal Semantics and Intentional States”. Analysis 64:3, pp. 215-223. July, 2004.

  • Doerge, F.C. (2006). Illocutionary Acts - Austin's Account and What Searle Made Out of It. Tuebingen 2006.

  • Grice, H. P. (1975). “Logic and Conversation”. In Syntax and Semantics 3: Speech Acts, ed. P. Cole & J. L. Morgan. New York: Academic Press.

  • Gronbeck, B., Ehninger, D., and Monroe, A.H. (1988). Principles of Speech Communication. New Jersey: Scott, Foresman.

  • Khouja M., Carmichael T., Saric A., Eichelerger C., Sun, M., and Hadzikadic, M. (2008). “A Computer Simulation Laboratory for Social Theories”. In Proceedings, Web Intelligence and Intelligent Agent Technology, 2008. Charlotte, NC, Dec 2008.

  • King, R. (2009). On the role of assertions for conceptual modeling as enablers of composable simulation solutions. Doctoral dissertation from Old Dominion University, Modeling, Simulation and Visualization Engineering Department, College of Engineering. Norfolk, VA.

  • Lewin, Kurt (1938): The Conceptual Representation and the Measurement of Psychological Forces. Contributions to Psychol­ogical Theory, 4, Duke University Press, Durham, N.C., 1938.

  • Lewin, K. (1945). "The Research Center for Group Dynamics at Massachusetts Institute of Technology". Sociometry8 (2)

  • Ogden, C.K. and Richards, I.A. (1923). The meaning of meaning. New York: Harcourt,Brace & World, Inc.

  • Poproski, R. (2010). “The Rationalizablity of Two Step Choices”. Journal of Philosophical Logic, 39:6, pp 713-743.

  • Saussure, Ferdinand de (1916), "Nature of the Linguistics Sign", in: Charles Bally & Albert Sechehaye (Ed.), Cours de linguistiquegénérale, McGraw Hill Education.

  • Searle, J. (1969). Speech Acts. Cambridge University Press.

  • Searle, J. (1975). “A Taxonomy of Illocutionary Acts”. In Gunderson, K., Language, Mind and Knowledge. Minneapolis.

Works cited 2

Works Cited (2)

  • Searle, J. (1979). Expression and Meaning. Cambridge University Press.

  • Shannon, C.E., and Weaver, W. (1949). The mathematical theory of communication. Urbana, Illinois: University of Illinois Press.

  • Snyder, M., & Swann Jr, W. B. (1978). Behavioral confirmation in social interaction: From social perception to social reality. Journal of Experimental Social Psychology, 14(2), 148-162.

  • Thagard P. (2000). Coherence in Thought and Action. Cambridge, Massachusetts: MIT Press 2000.

  • Tolk, A., Turnitsa, C.D., Diallo, S. (2008). “Implied Ontological Representation within the Levels of Conceptual Interoperability Model,” International Journal of Intelligent Decision Technologies (IDT). Volume 2, Issue 1, pp. 3-19, January 2008.

  • Turnitsa C., Gustavson P., Blais C. (2010). “Exploring Multi-Resolution Human Behavior Modeling Using Base Object Models”. In Proceedings, Fall Simulation Interoperability Workshop,2010, Simulation Interoperability Standards Organization, Orlando Florida, September 2010.

  • Turnitsa, C. (2013) “Representing the characteristics of modeled process,” In Proceedings, Winter Simulation Conference WSC’13, Washington DC, December 2013

Thank you

Charles Turnitsa

[email protected]

Thank You

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