Perceptual Control Theory as a Framework for Computer Modelling Across the Social Sciences

Perceptual Control Theory (PCT) as a Framework for Computer Modelling Across the Social SciencesDr Warren MansellSenior LecturerSchool of Psychological SciencesUniversity of Manchester

Credits to Bill Powers, Tim Carey, Rick Marken, Kent McClelland, Yu Li, Savas Akgonul, Sara Tai, Martin Brown, Dominic Rogers, Eric Gruber, Christine Ihenacho, Jason Wright, Hannah Gaffney, Rachel EdwardsPlanMy BackgroundWhat is Perceptual Control Theory (PCT)?Working examplesEconomicsSociologyLinguisticsModelling Goal Conflict in PsychopathologyMYLO Manage Your Life OnlineFurther discussion

My BackgroundResearch into Cognitive Behavioural Therapy since 1994Dissatisfied with cognitive & behavioural theoriesCame across PCT in 1998Use for psychotherapy (Method of Levels)Basic Research with in house computer software developer Yu Li Aim of talk wide applicability & demonstrate integrative capacity of PCTDemonstrationCan you tell what someone is doing by watching what they are doing?Rubber Band DemoHomeostasisClaude Bernard (1865)Walter Cannon (1932)

Control EngineeringHarold Black (1927)CyberneticsWiener (1948)Ashby (1952)Information theory

Cognitive Psychology

Perceptual Control TheoryWilliam T. Powers (1960)

Science Fictione.g. cyberspace

Self-Regulation Theorye.g. Carver & Scheier (1981)

Ancient technologyKtesibios (c200 BC)Heron (10-70AD)

Early PsychologyWilliam James (1890)John Dewey (1890s)

HistoryJohn Dewey (1896) the reflex arcWhat we have is a circuit, not an arc or broken segment of a circle. This circuit is more truly termed organic than reflex, because the motor response determines the stimulus, just as truly as sensory stimulus determines the movement. (Dewey, 1896; p. 363).History of Control Engineering

Perceptual Control Theory (PCT)Developed during the 1950s by a physicist/engineer William T. PowersFirst published Powers, Clark & McFarland (1960)Formalised Powers (1973)Powers latest Book (2008) reviewed in Nature (Mansell, 2008)Diverse range of applications published across academic domains (see www.pctweb.org)8Core Principles of PCTLiving organisms control their input, not their outputBehaviour is (merely) the control of perceptionAnalogous (and possibly homologous) to homeostatic mechanisms in physiological systemsEngineering principles can be used to explain these mechanisms of control 9Example within speech (Cziko)Make a /t/ soundNotice where your tongue is placedRepeat with tongue pressed against bottom of mouthCan you still say /t/? ability to speak comprehensibly with tongue in abnormal position shows that speech sounds are not pre-programmed motor outputs same phenomenon demonstrated when talking with cigarette, eating utensil or other object in the mouth (e.g.., food)NegativeFeedbackLoop

Powers (2008)

Basic Tracking Experiment Psychological Review (Powers, 1978) High negative correlation (-.99) between invisible disturbance (IV) and behaviour (DV)Low correlations (0.0) between disturbance and input and between input and behaviour not a linear causal modelPCT computer model provides 0.99 correlation with actual behaviour; replicated many times (Marken; Bourbon; see pctweb.org)

Principles of PCTControl is achieved via negative feedbackControl is achieved via a specific hierarchical organisation of loopsIndividuals can only control their own perception; controlling others leads to conflictConflict between high level control systems accounts for dysfunction Reorganisation re-establishes control via a specific learning mechanismEconomics: Modelling Market AgentsMcClelland (2010). www.pctweb.org

Rational choice model of economic agents insufficientModelled a range of risky & conservative strategies robot investorsRelative advantages depended on economy modelled

McClellands proposed model: Investors try to control two perceptions.Investment growthThey want to see steady growth in the value of their investments.

Liquidity protectionThey want to see steady growth (or no decline) in their liquid assetscash.Both perceptions are rates of change. These two perceptions are sometimes in conflict.To see your investments grow, you need to get into the market and buy.

To increase your supply of cash, you need to sell some of your investments. You cant have it both ways at once, but both goals are desirable.PCT Model

Above the lighter dotted line:Our two control systems

Between the two dotted lines:Perceptions controlled by lower-level systems (not explicitly modeled)

Below the heavier dotted line:Variables and relationships in the external environment.

We treat the market price of the investment as a disturbance.Heres the price of the fictitious stock. It follows the ups and downs of the Dow-Jones average from 2000 to 2010. There is a 6% drop overall.Here are the starting values for the robot investors. Each investor starts with $200,000 in assets: Stock worth $100,000 (1000 shares at $100 a share) and $100,000 in cash.

Minimum transaction is 50 shares bought or sold.

Each run of the simulation is 520 iterations (weeks).

see what theyre worth at the end of ten years. One example - DerekDereks profile:

How did Derek do?997 shares: $93,621.54 Cash: $110,953.38Total assets: $204,574.922.3% GAIN

Control SystemReference ValueSystem GainInvestment Growth15%1000Liquidity Protection15%1000How Derek did itHe bought when the price was going down and sold when the price was going up. 2121Contributions of this study to theories of economic behaviorThe agents demonstrate that an actor can appear rational without having any ability for rational deliberation.

The findings call rational choice theory into question, since none of the assumptions for rational choice theory are satisfied by the robot investors.

PCT offers an intriguing alternative to the received wisdom about economic behavior. Emergent Group ProcessesComputer simulations of multiple agents Each formed from control systems with a reference and gain for a variable e.g. proximity to othersMcPhail, Powers & Tucker (1992) - demoSimulation Run A14 SAs were given 2 identical goals:-1) avoid collisions with anything in their path2) pursue the target actor (PT)

Co-ordinates of origin of targetCo-ordinates of origin for SAsOUTCOME:Ring FormationSimulation Run B

Randomly distributed obstacles introduced

OUTCOME:Ring formationEffectively guided round obstaclesCo-ordinates of origin for SAsStationary ActorsAtypicality of Runs A & BThe formations evident in Runs A & B were atypical for 2 reasons:

1) The rings formed were very symmetrical, and this is unlikely to happen in the non-simulated world.

2) Most gatherings are not made up of solitary individuals, rather they are made up of companion clusters such as families or friendship groups.

Therefore, the next run was programmed differently.All SAs (except the target actor) were programmed to follow another actor, to make asymmetric pairs, with the latter member of each pair pursuing PT (the target).[ SA1 SA2 PT]

Almost perfect circleSimulation Run C & D

Run C (with asymmetrical pairs) ended in a double arc form.Again this is rarely observed in public places.Run D included 15 obstacles (stationary actors) and this caused some of the pairs to get separated.This resulted in a less symmetrical, more authentic ring that better approximates social forms observed in the real world.Modelling Social Interactions (Mansell et al., in prep)McPhail et al. used quantitative data to generate qualitative outcomesCan a PCT model be used to generate quantitative models of actual behaviour? Personal Distance Paradigm

The Method 45 participants 5 participants each trial Each participant pairs up for a conversation with each of the other 4 participants Video camera used to record personal distance for each pairing

There was a total of 45 participants in the study-5 participants in each trial who were numbered 1-5 on arrival to Coupland one building -5 participants waited outside on chairs.-Measuring personal space- the two participants were brought randomly into the room and asked to stand on 2 xs which were located at either end of a 3 metre marked walkway. -While being filmed they were asked to talk about a topic related to university life such as the best student nights out in ManchesterAnd walk towards one another stopping at a distance they felt comfortable with.-When the participants had reached a distance and stopped fluctuating it was recorded by a researcher.Each participant did this four times.29The conversation task analysis pairings of participants as A or B

Participant123451ABBA2ABB3AB4A5-For each 5 participant trial the participants were allocated two A and 2 B conditions.

-This was done so that to ensure that each participant was randomly assigned to either of the conditions twice.

-For the correlation this enabled half of the participants to be classified as A personal distance scores which could then be correlated with the other half of B scores.

-For the computer modelling the A pairs were used to predict the B pairs

30AnalysisComputer model of two feedback loops controlling the same input (personal distance)Estimate Reference Value (R) from the mean of the distance in A pairingsUse trial & error changes to select Rs and Gains (G) that generate the measured distance as their inputInput values of R and G into model to simulate novel pairings

The PCT computer program (Mansell et al, 2011)

-Has all the functions, signals and amplification factor of gain (Input and output gain)

-Adjust the reference value

-Adjust the gain

2 control systems

-Until desired input achieved = measured personal distance

Reference ValueReference ValueOutput GainReference ValueInput Quantity

This is the PCT computer program, as you can see the reference value is inputted at the top, the system gain at the right hand side, and the input value is at the left hand side.

So beginning two participants that make up a pair two estimated reference values and a low system output gain of 4.0 were entered for each negative feedback loop.

The reference value and gain for the two control systems was changed until the input that was obtained for the pair was achieved.

.32HypothesisThe estimated personal distance generated by computer models trained on A pairings will generate an estimate of personal distance for B pairings that is closely correlated with actual personal distance

r2=0.32The computer model(r = .563, n= 44, p