Download Cognitive Agent-based Models

ETH D-GESS: 851-0585-37L
Social Modelling, Agent-Based
Simulation and Collective Intelligence
(Week 11)
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
1
Cognitive Agent-Based Models
ETH D-GESS: 851-0585-37L Week 11
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
2
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!
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
3
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
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
4
“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)
Graphic from https://commons.wikimedia.org/w/index.php?curid=1918592; “Dualism”
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
5
In general, a Cognitive Architecture is a Control System
(Inspired by Piaget, 1985)
A system of components and mechanisms whose purpose is to control an intelligent actor.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
6
In particular, it is a Control System with Adaptive Memory
(Image after Anderson, 1983)
And it can be a system that may, or may not, account for the emotions of the agent actor.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
7
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)
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
8
State Operator and Result (Soar)
Inputs from World
Outputs to World
http://www.slideshare.net/diannepatricia/laird-ibmsmall
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 |
9
Outputs to
World
Belief, Desire, and Intention
(BDI)
Inputs from
World
Bratman, et al., 1988, p. 7, Fig. 1.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 10
Physis, Emotion, Cognition, Social Status (PECS)
Inputs from
World
Outputs to
World
Outputs to
World
Outputs to
World
Urban and Schmidt, 2001, p. 2, Fig. 1.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 11
ACT-R
Inputs from World
Outputs to World
Anderson, 1993
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 12
CLARION
Inputs from
World
Outputs to
World
MS = motivational subsystem
MCS = meta-cognitive subsystem
ACS = action-centered subsystem
NACS = non-action-centered subsystem
Sun, 2004, p. 2, Fig. 1.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 13
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)
Epstein, 2013, “Skeletal Equation”, p. 6-8.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 14
tmrEngine
Rouly, 2007- Parallelized Turing P-Type automata
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 15
What Do They All Have In Common?
Inputs from World
Outputs to World
http://www.slideshare.net/diannepatricia/laird-ibmsmall
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 16
After the break will we continue our discussion of cognitive agentbased 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.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 17
break
5-6 minutes
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 18
"Things should be made as simple as possible - but no simpler."
Albert Einstein
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 19
Cognitive-Agent Based Modeling
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 slowspeed 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.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 20
The Models
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 21
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. }
Concepts:
CSS modeling paradigm – none
Simple tools – none
Research hypothesis – An automated problem solver creates and subsumes goals.
Soar simulates aspects of human cognition: it “chunks,” sub-goals, and learns.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 22
Crime reduction through simulation: An agent-based model of
burglary, (Malleson, Heppenstall, & See, 2010)
Agent properties/rules:
{ Heuristic model
incorporating integrated agent
physical, emotional, cognitive,
and social status. Nonadaptive, high-speed, reactive
engine.
Concepts:
CSS modeling paradigm – Spatial ABM
Simple tools – Heuristic design
Research hypothesis – High-speed and realism in behavior within practical models.
Urban crime simulation and hypothesis testing in a compact, high-speed model.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 23
ACT: A simple theory of complex cognition, (Anderson, 1996)
Agent properties/rules:
{ Adaptive Control of ThoughtRational (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.
Concepts:
CSS modeling paradigm – none
Simple tools – none
Research hypothesis – This is an algorithm that mimics human associative memory.
Anderson, 1993, p. 356, Fig. 2.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 24
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.}
Concepts:
CSS modeling paradigm – none
Simple tools – none
Research hypothesis – The algorithm will perform at least as well as a human.
Sun and Naveh, 2004, Tables 1 & 2 combined.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 25
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.
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.
Agent learning (and survival) was driven by individual need and steered by the
Maslow Hierarchy of Needs.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 26
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.
Week 11 deliverables: Reading and accountability
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 27
REFERENCES
• 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).
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 28
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.
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 29
In the weeks that follow we will:
 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
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 30
Contact information
ETH Zurich
D-GESS Computational Social Science
Clausiusstrasse 50
8006 Zürich, Switzerland
http://www.coss.ethz.ch/
Ovi Chris Rouly, PhD.
Email: orouly@ethz.ch
Telephone: (41) 044-633-8380
© ETH Zurich, 3 May 2016
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 31
LAST SLIDE
Department of Humanities, Social and Political Sciences
Program in Computational Social Science
Ovi Chris Rouly, PhD | 03.05.2016 | 32