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||Department of Humanities, Social and Political Sciences

Program in Computational Social Science03.05.2016 1

ETH D-GESS: 851-0585-37L

Ovi Chris Rouly, PhD

Social Modelling, Agent-Based

Simulation and Collective Intelligence(Week 11)


Program in Computational Social Science03.05.2016Ovi Chris Rouly, PhD 2

Cognitive Agent-Based Models

ETH D-GESS: 851-0585-37L Week 11



The agents in our models are encapsulated software objects. This

object-oriented approach lets us instantiate anthropomorphized

agent “actors” that are separate from the model topology within and

or upon which the social system may exist, e.g. as cellular automata,

spatial-agents, or purely “logical” agents. Because of this

approach, we can give our agents behavioral rules (instructions for

behavior) and properties (quantitative and qualitative, and fixed and

adaptive) that make them not just plausible and highly-descriptive, but

also analytically separable from their underlying model topology.

In this lesson we consider cognitive agents in particular. Cognitive

agents tend to have more fully developed cognitive (and or emotional)

behaviors, but also tend to occupy significant amounts of memory and

execute more slowly than non-cognitive types. What are the tradeoffs?

Let’s get started!



Course Overview

Procedure (Parts I & II):

1. Examine a selection of published, formal models of social processes

2. Learn how to analyze and extend simple models and to develop your own

social process models using existing computer-coded examples

Social Modelling, Agent-Based

Simulation and Collective Intelligence


Program in Computational Social Science

Graphic from https://commons.wikimedia.org/w/index.php?curid=1918592; “Dualism”

03.05.2016Ovi Chris Rouly, PhD 5

“Models” of cognition may have begun with Plato*,

passed thru Descartes** and are now studied formally within

Cognitive Psychology and Cognitive Science

• Cognitive architecture: A theoretic

representation describing aspects of

the structure of the mind; usually one

having natural intelligence.

• Cognitive model: A (possibly) instantiable

representation of an agent control

mechanism resembling a cognitive

architecture.

• Typical cognitive architectures:

Symbolic, heuristic, and logical

Connectionist (neural networks)

Hybrids and others

* Plato, Republic, Allegory of the Cave (ca. 400 BC)

** Descartes, Treatise of Man – “Dualism” (ca. 1600 AD)



A system of components and mechanisms whose purpose is to control an intelligent actor.


In general, a Cognitive Architecture is a Control System

(Inspired by Piaget, 1985)



And it can be a system that may, or may not, account for the emotions of the agent actor.


In particular, it is a Control System with Adaptive Memory

(Image after Anderson, 1983)



A Few Examples of Cognitive Engines/Architectures

Soar: State Operator And Result

(Newell, Laird, Rosenbloom, ca. 1987)

BDI: Belief, Desire, and Intention / PECS: Physis, Emotion, Cognitive, Social

(Bratman, 1988) (Urban, 2001)

ACT-R: Adaptive Control of Thought – Rational

(Anderson, ca. 1996)

CLARION: Connectionist Learning with Adaptive Rule Induction On-line

(Sun, ca. 2006)

Agent Zero: A { 0 , 1 }

(Epstein, 2013)

tmrEngine: Turing, Maslow, Rouly Engine

(Rouly, 2007 - current)



http://www.slideshare.net/diannepatricia/laird-ibmsmall


State Operator and Result (Soar)

Outputs to WorldInputs from World



Bratman, et al., 1988, p. 7, Fig. 1.


Belief, Desire, and Intention

(BDI)

Inputs from

World

Outputs to

World



Urban and Schmidt, 2001, p. 2, Fig. 1.


Physis, Emotion, Cognition, Social Status (PECS)

Inputs from

World

Outputs to

World

Outputs to

World

Outputs to

World



Anderson, 1993


ACT-R

Inputs from World Outputs to World



Sun, 2004, p. 2, Fig. 1.


CLARION

Inputs from

World

Outputs to

World

MS = motivational subsystem

MCS = meta-cognitive subsystem

ACS = action-centered subsystem

NACS = non-action-centered subsystem



Epstein, 2013, “Skeletal Equation”, p. 6-8.


Agent Zero

Total disposition “D” of agent “i” at time “t” and relative to agent “j”.

Where:

ω is an arbitrary measure of importance (agent-to-agent)

V is an affective measure (relative emotion)

P is a deliberative measure (relative cognition)



Rouly, 2007- Parallelized Turing P-Type automata


tmrEngine



http://www.slideshare.net/diannepatricia/laird-ibmsmall


What Do They All Have In Common?

Inputs from WorldOutputs to World



After the break will we continue our discussion of cognitive agent-

based models. So far, we have focused only on formalisms related

to human prototypes. However, other social and highly-intelligent

animals might be modeled if we operate by inference since most

other species appear to be unable to self-report. For example,

perhaps the non-human species of primate, some species of dog or

wolf, and or whales and dolphins, etc., might be modeled by

abstract and or explicit means, if we can sufficiently account for

their respective forms of intelligence and sociality.

There is no new writing assignment. However, one is pending.

There are two reading assignments that will appear on the exam.



5-6 minutes


break



Albert Einstein


"Things should be made as simple as possible - but no simpler."



1. Goal: model the cognitive behaviors of humans.

2. Hazard: cognitive architectures tend to be large, slow, and

arbitrary.

3. Worst Result: because of their typically large size and slow-

speed few cognitive architectures are used with ABMs.

4. Best Result: create a cognitive architecture that simulates

human reasoning; is small, fast and will operate in any

agent-based model.

Cognitive-Agent Based Modeling



The Models



Soar simulates aspects of human cognition: it “chunks,” sub-goals, and learns.


Concepts:

CSS modeling paradigm – none

Simple tools – none

Research hypothesis – An automated problem solver creates and subsumes goals.

Soar: An architecture for general intelligence (Laird, Newell, & Rosenbloom, 1987)

Agent properties/rules:

{ Soar is a cognitive engine that

relies on a list of if/then rules

called productions. The

problems is solves are called

goals. If it cannot solve a problem

due to the lack of sufficient

productions, then it sub-goals.

That is, it creates new goals. Soar

“chunking,” or links, related

productions together. }



Urban crime simulation and hypothesis testing in a compact, high-speed model.


Concepts:

CSS modeling paradigm – Spatial ABM

Simple tools – Heuristic design

Research hypothesis – High-speed and realism in behavior within practical models.

Crime reduction through simulation: An agent-based model of

burglary, (Malleson, Heppenstall, & See, 2010)


{ Heuristic model

incorporating integrated agent

physical, emotional, cognitive,

and social status. Non-

adaptive, high-speed, reactive

engine.



Anderson, 1993, p. 356, Fig. 2.


Concepts:



Research hypothesis – This is an algorithm that mimics human associative memory.

ACT: A simple theory of complex cognition, (Anderson, 1996)


{ Adaptive Control of Thought-

Rational (ACT-R) is an algorithm

designed to mimic associative

memory in humans. It that relies on

rules (nodes) representing

assumptions about the environment

of ACT-R. Nodes with higher-levels

of “activation” (similarity to aspects

of the problem under consideration)

spread their influence and may

result in a problem solution.



Sun and Naveh, 2004, Tables 1 & 2 combined.


Concepts:



Research hypothesis – The algorithm will perform at least as well as a human.

Simulating organizational decision-making using a cognitively

realistic agent model

(Sun and Naveh, 2004)

Agent properties/rules: { test involved identifying

“blips” on a radar,

D=distributed information

access among team,

B=blocked information

access, Human and

CLARION are roughly

equivalent.}



Agent learning (and survival) was driven by individual need and steered by the

Maslow Hierarchy of Needs.


Concepts:

CSS modeling paradigm – Cognitive agent-based model

Simple tools – Spatial constraints, prioritized drives, social preferences

Research hypothesis – A parallelized P-Type engine will adapt to a social setting.

Learning automata and need-based drive reduction (Rouly, 2007)

Agent properties:

{ hunger/satiety, olfaction/odor, single-step

moves, Maslow prioritized drives, individual

Turing P-Type learning automata }

Rules:

Each agent asynchronously moves in an

attempt to survive in the maze. Predators

and prey have unique scents that their

opposites can identify. Agents learn

independently.

movie/DarylMouseRunning.mp4



Week 11 deliverables: Reading and accountability


Deliverables this week

Reading assignments:

Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback,

S. F. (2010). The ODD protocol: a review and first update. Ecological

modelling, 221(23), 2760-2768.

Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C.,

Harburger, J., ... & Parker, M. (2002). Population growth and collapse in a

multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings

of the National Academy of Sciences, 99(suppl 3), 7275-7279.

Writing/Coding assignment:

None.



• Anderson, J. R. (1983). A spreading activation theory of memory. Journal of verbal learning and

verbal behavior, 22(3), 261-295.

• Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51(4), p.

355.

• Bratman, M. E., Israel, D. J., & Pollack, M. E. (1988). Plans and resource‐bounded practical

reasoning. Computational intelligence, 4(3), 349-355.

• Epstein, J. M. (2014). Agent_Zero: Toward Neurocognitive Foundations for Generative Social

Science. Princeton University Press.

• http://www.slideshare.net/diannepatricia/laird-ibmsmall accessed on 1 May 2016, 20:15

• Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence.

Artificial intelligence, 33(1), pp. 1-64.

• Malleson, N., Heppenstall, A., & See, L. (2010). Crime reduction through simulation: An agent-based

model of burglary. Computers, environment and urban systems, 34(3), 236-250.

• Piaget, J. (1985). The equilibration of cognitive structures: The central problem of intellectual

development. University of Chicago Press.

• Plato, Plato, & Halliwell, S. (1988). Republic 10. Aris & Phillips.

• Rowlands, M. (1999). The body in mind: Understanding cognitive processes. Cambridge University

Press.

• Rouly, O. C. Learning Automata and Need-Based Drive Reduction. In Proceedings of the 8th

International Conference on Intelligent Technologies (pp. 310-312).

REFERENCES



• Sun, R., & Naveh, I. (2004). Simulating organizational decision-making using a cognitively realistic

agent model. Journal of Artificial Societies and Social Simulation, 7(3).

• Sun, R. (2006). The CLARION cognitive architecture: Extending cognitive modeling to social

simulation. Cognition and multi-agent interaction, p. 79-99.

• Urban, C., & Schmidt, B. (2001). PECS–Agent-Based Modelling of Human Behaviour. In Emotional

and Intelligent–The Tangled Knot of Social Cognition, AAAI Fall Symposium Series.

• Vernon, D., (2014). Artificial Cognitive Systems – A Primer, MIT Press.

REFERENCES



we will see models of pedestrians and traffic

models of abstract social systems and a historical culture

consider explicit models and their explanatory utility

and, decide if we think Collective Intelligence can be instantiated

In the weeks that follow we will:



ETH Zurich

D-GESS Computational Social Science

Clausiusstrasse 50

8006 Zürich, Switzerland

http://www.coss.ethz.ch/

Ovi Chris Rouly, PhD.

Email: [email protected]

Telephone: (41) 044-633-8380

© ETH Zurich, 3 May 2016


Contact information

http://www.coss.ethz.ch/

mailto:[email protected]



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