Lecture 12: Agent Societies

Two perspectivesCoordination in MAS by norms and social laws (textbook, Ch. 9.6.4)Using MAS for simulation of social phenomena

Coordination in MAS by norms and social lawsConventions, Norms: an established, expected pattern of behaviore.g. queuing in the end of the queue, using a certain language to communicate with othersSocial laws: norm associated with authority enforcing them and punishment in case of violatione.g. laws protecting private property

Provide template behaviors which save reasoning power, and behavioral constraints that assure that agents have a chance to achieve their goals

Conceptualizations of norms within game theoryNorms as solutions to problems of co-ordination Norms as solutions of conflict of utility Norms as solutions to problems of inequality

Two origins of norms and social laws in MASEmbedded in the design on each agent. Social laws and norms are designed offline and hardwired in each agent. Examples: Tennenholtz, Conte & CastelfranchiEmerging from within the interactions of self-interested but cooperative agents. Examples: Shoham & Tennenholtz

Embedded norms and lawsEngineering multi-agent organizationsUsually hierarchical design: who reports to whom; predefined authority structuresModeled after human organizationsDefines the flow of information / control and the agent interactionsMuch like Software Engineering or like Protocol design for negotiations

Design of a social law: exampleOnly one robot can occupy a grid-point at any timeRobots collect and transport items from one grid-point to anotherDesign a social law that prevents collisions

One possible social lawEach robot has to move constantlyDirection of motion fixed on alternating rowsMove-up when at right-most columnMove-down when eitherOn the left-most column of even rowsOn 2nd rightmost column of odd rows012345Next move of robot is uniquely definedRobot can always get to the desired locIn at most O(n2) movesBut not efficient!

Emergent normsHow can a norm or social law emerge in a society of agents?How can agents reach a global agreement by using only local information Each agent decides which convention to follow based only on its own experience?The T-shirt game (Shoham & Tennenholtz, 1992): can all agents agree on the same colour?

Emergent norms (2)Strategy update functions for the T-shirt game: history of observed colours choice of colourSimple majoritySimple majority with agent types agents of the same type can communicate, exchange historiesSimple majority with communication on success (after an agent has reached a success threshold)Highest cumulative rewardMeasuring the efficiency of convergence, how many rounds it takes for all agents to converge to a particular strategyResults: all strategies lead to the emergence of conventionsThe strongest results about the highest cumulative reward update function. It can be shown that agreement can be reached with certain probability in finite number of rounds.

Using MAS for simulation of social phenomenaWhy to adopt computer-based social simulation?Social phenomena are not directly accessible or are difficult to observe.Some social studies are time consuming, policy study spans a considerable time.Some social studies cannot be replicated in labs; difficult to collect data, e.g. crime study: bribery and deterrence

Computational vs Sociological Study off Norms

Computational study of norms is a formal approach to theory building in the field of norms Sociological study of norms aims at: appropriate definition of norms explanation of how norms affect social behaviour explanation of how norms emerge

Sociological Study of NormsFour different conceptualization of norms:The statistical conceptualization of norms originates in behaviourism. A behavioural pattern becomes a norm if the majority of actors behave according to this pattern.According to Durkheim norms are social facts which can be identified through the mere existence of certain sanctions.According to Ethnomethodologists: several basic rules which have a pseudo-normative character seen as obliging and deviations are sanctioned deviations from these basic rules is judged in clinical, not moral categoriesDevelopmental psychologists have put forward an ethical conceptualization of norms

Sociological Study of Norms (contd)Functions of Norms: Norms have been analyzed as solutions to social problems According to Marx, norms are dependent on the economic foundations of society.Computational study of norms develops into sociological Computational development of concepts advances from the statistical to the sociological conceptualization of norms.

The Conte and Castelfranchi Model

Background:50 agents are placed randomly into a 2-dimensional world that consists of a 10*10 grid with connected edges (a torus). The initial strength of each agent is 40. 25 food items of nutritional value 20 are distributed randomly on the grid. Each food item is replenished at a randomly selected location on the grid after it has been consumed. At the beginning of a match, agents are randomly allocated to locations and are assigned those food items which happen to fall into their own territories (their von Neumann-neighborhood). Food possessed is flagged and each agent knows to whom it belongs.

Types of actions in the Conte and Castelfranchi model

Conte and Castelfranchis modelActions are simultaneous. Depending on built-in strategies and knowledge, agents may decide to attack one another.Three strategies: blind aggression (attack an eater to get its food, unless free food is available at a lower cost) strategic aggression (attack an eater whenever you perceive it as no stronger than you, unless free food is available at a lower cost) normative aggression (attack an eater unless the food item being eaten is marked as owned by that agent I.e. the finder-keeper norm)

Conte and Castelfranchis experimentExperiment consists of 100 matches, each of which includes 2000 games. During each game each agent performs one action.For each experiment, the number of attacks, the average strength, and the standard deviation of individual strength is recorded.Results:In homogeneous population the agents using the normative strategy do best at controlling aggression and keeping inequality lowIn mixed population the normative strategy becomes the worst

Resimulating norms by Saam & Harrer Difference between the original model and the re-implemented model actions are not executedsimultaneously, but in sequence. This decreasesthe number of conflicts and thus results are lessdependent on random resolutions of conflictingactions.Replication results are the same as the originalresults.

Saam and Harrer (Experiment 1)First Experiment: Private Property and HeritageExtended the Conte and Castelfranchi model agents may reproduce and the offspring inherit the sum of the strength of their parents.The agent chooses another agent who is next to it within its von Neumann-neighbourhood, unite their strength, produce two children, divide their total strength and forward it to the children. The parents die immediately and the children take their places in the grid.

Saam & Harrer (1): resultsResults: For equal heritage, the normative strategy is found to perform best at increasing the average strength of the agents, reducing inequality among them and reducing aggression.For unequal heritage, the normative strategy leads in producing the worst inequality.

Saam & Harrer: experiment 2Second Experiment: Unequal Renewal of ResourcesIn this model nutritional value of food is no longer constant. When a food item is replenished and happens to fall at the same location as an agent, the nutrition value depends on the strength (s) of the agent. The higher the strength of the agent during the previous time step, the larger is the nutrition value of the replenishing food item:Resimulating

Saam & Harrer (2): resultsThe normative MAS has the highest average strength and the lowest degree of aggression.Compared to the original model, inequality is much more pronounced.In sum, in homogeneous societies, the finder-keeper norm:minimizes aggression in all experimentsmaximizes the average strength of the agents in all experimentsHowever its function with respect to equality depends very much on the initial conditions and the redistribution of strength.

Resimulating Norms from Sociological Theoryby Saam and HarrerHaferkamps theory of action approach to deviant behaviourHaferkamp combines the theory of action and system theory and integrates power and rule as conflict theoretical elementsHe defines norms as conceptions which are internalized by the majority of members of a social situation. The conception implies the correct (re)actions to defined situations and the certitude that deviance will be sanctioned.

Sociological ModelThe agents live in a two-group society, in-group g1 and out-group g2. Members of the in-group are more resourceful and powerful than members of the out-group.Those individuals who are most powerful rule and therefore decide about the institutionalization of norms in society as a whole. Members of the in-group transfer some of the resources to the redistribution agent in exchange for the institutionalization of norm n1 in situation s1.The redistribution agent redistributes the resources uniformly to all agents.The agents are able to identify and define social situations. Deviant behaviour is sanctioned by the members of the in-group. Agent a1 will sanction agent a2 if it observes it reacting by behaviour b2 (instead of behaviour b1) to situation s1. Whenever an agent of the in-group sanctions an agent of the out-group this will increase its power and decrease its resources.

Sociological Simulation Results

In the mixed population case, the normative strategy (finder-keeper) no longer generates the worst inequality (as in the Castelfranchi, Conte and Paolucci experiments).The finder-keeper norm no longer controls aggression the most effectively.Problem with the model: it is incomplete with respect to the power variable. The power of the in-group agents increases continuously, whereas the power of the out-group agents remains constant. Power is not consumed.

A Simulation of the Market for Offenses in Multiagent Systems: Is Zero Crime Rates Attainable?Pinata Winoto

Presented at MABS2002 Workshop with AAMAS02, Bologna

MotivationsMany theories have been developed to understand criminal behavior at micro level and crime market at macro levelIn an open multiagent system, an optimal policy against malevolent actions is neededHard to test a theory, especially in the macro level

Are Existing Theories Suitable for MAS?Micro-level:Agents are less complicated, more consistent, and more homogeneous than people (No drugs, alcohol, social dilemma, etc.)Most existing theories are developed under assumptions which fit MASMacro-level:MAS is a discrete world, can be initialized, consists of small number of agentsExisting theories assume continuous domains with large number of agents existing for a long time

ObjectiveFenders equilibrium theory (Journal of Economic Behavior and Organization, 1999)Multiple equilibria of crime rate existOne of the stable equilibria is low crime rate (zero crime)Specific conditions to attain zero crime rate equilibrium are unknownObjective: Exploring zero crime rate equilibrium

Fenders Equilibrium TheoryPotential offenders follow rational choice theory (von Neumann-Morgenstern Expected Utility):commit crime if their expected return from crime is greater than that from legitimate workSome agents will not commit crime under any circumstance (Honest Agents)Governments expenditure in controlling crime is financed by the tax collected from the workers

Fenders Equilibrium Theory (cont)Agents expected return from crime:Uc = p u2 + (1-p) u1p : probability of conviction/punishedu2 : return from crime if convicted (fixed)u1 : return from crime if not convicted (fixed)Agents return from legitimate work:Uw = w - L - Tw : return from work (uniform distribution)L : expected loss from being victimizedT : average tax paid to government

Fenders Equilibrium Theory (cont)Agents expected loss from being victimized:L = lC/(n-C)l : average loss from crime (fixed)C : number of criminalsn : number of agents (fixed)n - C : number of legitimate workersAverage tax to be paid by agents:T = E/(n-C)E : total governments expenditure (fixed)

Fenders Equilibrium Theory (cont)The effectiveness of the law enforcement in controlling crime depends on the punishment and the probability of conviction:p = G(E) / CG(E) : number of criminals convicted (productivity of government spending on the law enforcement)Multiple equilibria exist, where two of them are stable equilibria: high crime rate and low crime rate equilibria

An Example of Fenders ConjectureA/D are stable low/high crime rate equilibriaB is an unstable equilibrium: higher/lower probability of conviction or lower/higher number of criminals will drag the system to A/D

Chart4

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B(166; 0.6)

A(0; 1.0)

D

EC

PP

# criminals

prob. Conviction

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56.3-2155.5410999.9999999971358180.250000021358180.25000002830.2274766069247.52252339317900.5389531680.12658227850.1265822785

56.4-2154407999.9999999971375716.000000021375716.00000002831.7273009118245.27269908828000.54444444440.1250.125

56.5-2152.5404999.9999999971393256.250000021393256.25000002833.2156905089243.03430949118100.55025210080.12345679010.1234567901

56.6-2151401999.9999999971410801.000000021410801.00000002834.6928606651240.80713933498200.55638418080.12195121950.1219512195

56.7-2149.5398999.9999999971428350.250000021428350.25000002836.1590200295238.59097997058300.56284900280.12048192770.1204819277

56.8-2148395999.9999999971445904.000000021445904.00000002837.614370914236.3856290868400.56965517240.1190476190.119047619

56.9-2146.5392999.9999999971463462.250000021463462.25000002839.0591095594234.19089044068500.57681159420.11764705880.1176470588

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57.1-2143.5386999.9999999971498592.250000021498592.25000002841.9175062389229.83249376118700.59221238940.11494252870.1149425287

57.2-2142383999.9999999971516164.000000021516164.00000002843.3315285997227.66847140038800.60047619050.11363636360.1136363636

57.3-2140.5380999.9999999971533740.250000021533740.25000002844.7356678152225.51433218488900.60912912910.11235955060.1123595506

57.4-2139377999.9999999971551321.000000021551321.00000002846.1300932944223.36990670569000.61818181820.11111111110.1111111111

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57.8-2133365999.9999999971621689.000000021621689.00000002851.6138837871214.88611621299400.65861635220.10638297870.1063829787

57.9-2131.5362999.9999999971639292.250000021639292.25000002852.9621219293212.78787807079500.66984126980.10526315790.1052631579

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58.2-2127353999.9999999971692129.000000021692129.00000002856.954647107206.5453528939800.70640522880.10204081630.1020408163

58.3-2125.5350999.9999999971709750.250000021709750.25000002858.268546319204.4814536819900.71960396040.1010101010.101010101

58.4-2124347999.9999999971727376.000000021727376.00000002859.574192535202.42580746510000.73333333330.10.1

58.5-2122.5344999.9999999971745006.250000021745006.25000002860.871711755200.378288245

58.6-2121341999.9999999971762641.000000021762641.00000002862.1612268363198.3387731637

58.7-2119.5338999.9999999971780280.250000021780280.25000002863.4428576017196.3071423983

58.8-2118335999.9999999971797924.000000021797924.00000002864.7167209433194.2832790567

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59.4-2109317999.9999999941903881.000000041903881.00000004872.2029859271182.2970140729

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59.6-2106311999.9999999941939236.000000041939236.00000004874.6411351737178.3588648263

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59.8-2103305999.9999999941974609.000000041974609.00000004877.0519534532174.4480465468

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60.4-2094287999.9999999942080836.000000042080836.00000004884.1275779804162.8724220196

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61-2085269999.9999999942187225.000000042187225.00000004890.9817439712151.5182560288

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61.4-2079257999.9999999942258241.000000042258241.00000004895.4361223149144.0638776851

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61.6-2076251999.9999999942293776.000000042293776.00000004897.6304266696140.3695733304

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61.8-2073245999.9999999942329329.000000042329329.00000004899.8034857658136.6965142342

61.9-2071.5242999.9999999942347112.250000042347112.25000004900.8822004871134.8677995129

62-2070239999.9999999942364900.000000042364900.00000004901.9557841937133.0442158063

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62.4-2064227999.9999999942436096.000000042436096.00000004906.1999487442125.8000512558

62.5-2062.5224999.9999999942453906.250000042453906.25000004907.2487232664124.0012767336

62.6-2061221999.9999999942471721.000000042471721.00000004908.2926980622122.2073019378

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63-2055209999.9999999942543025.000000042543025.00000004912.4216223912115.0783776088

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63.5-2047.5194999.9999999942632256.250000042632256.25000004917.4807391421106.2692608579

63.6-2046191999.9999999942650116.000000042650116.00000004918.4794220842104.5205779158

63.7-2044.5188999.9999999942667980.250000042667980.25000004919.4738283625102.7761716375

63.8-2043185999.9999999942685849.000000042685849.00000004920.4640008591101.0359991409

63.9-2041.5182999.9999999942703722.250000042703722.25000004921.449981755299.3000182448

64-2040179999.9999999942721600.000000042721600.00000004922.43181254697.568187454

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64.2-2037173999.9999999942757369.000000042757369.00000004924.383186459594.1168135405

64.3-2035.5170999.9999999942775260.250000042775260.25000004925.352809282892.3971907172

64.4-2034167999.9999999942793156.000000042793156.00000004926.318441431290.6815585688

64.5-2032.5164999.9999999942811056.250000042811056.25000004927.280121196288.9698788038

64.6-2031161999.9999999942828961.000000042828961.00000004928.237886270287.2621137298

64.7-2029.5158999.9999999942846870.250000042846870.25000004929.191773759785.5582262403

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64.9-2026.5152999.9999999942882702.250000042882702.25000004931.088061555482.1619384446

65-2025149999.9999999942900625.000000042900625.00000004932.030533256380.4694667437

65.1-2023.5146999.9999999942918552.250000042918552.25000004932.969270185278.7807298148

65.2-2022143999.9999999942936484.000000042936484.00000004933.90430670177.095693299

65.3-2020.5140999.9999999942954420.250000042954420.25000004934.835676647775.4143233523

65.4-2019137999.9999999942972361.000000042972361.00000004935.763413364473.7365866356

65.5-2017.5134999.9999999942990306.250000042990306.25000004936.687549696472.0624503036

65.6-2016131999.9999999943008256.000000043008256.00000004937.608118005270.3918819948

65.7-2014.5128999.9999999943026210.250000043026210.25000004938.525150178268.7248498218

65.8-2013125999.9999999943044169.000000043044169.00000004939.438677638567.0613223615

65.9-2011.5122999.9999999943062132.250000043062132.25000004940.348731354265.4012686458

66-2010119999.9999999913080100.000000053080100.00000005941.255341847863.7446581522

66.1-2008.5116999.9999999913098072.250000053098072.25000005942.158539204762.0914607953

66.2-2007113999.9999999913116049.000000053116049.00000005943.058353082160.4416469179

66.3-2005.5110999.9999999913134030.250000053134030.25000005943.954812717458.7951872826

66.4-2004107999.9999999913152016.000000053152016.00000005944.847946936857.1520530632

66.5-2002.5104999.9999999913170006.250000053170006.25000005945.737784162655.5122158374

66.6-2001101999.9999999913188001.000000053188001.00000005946.624352421853.8756475782

66.7-1999.598999.99999999093206000.250000053206000.25000005947.507679353352.2423206467

66.8-199895999.99999999123224004.000000053224004.00000005948.387792215450.6122077846

66.9-1996.592999.99999999093242012.250000053242012.25000005949.264717893248.9852821068

67-199589999.99999999093260025.000000053260025.00000005950.138482905847.3615170942

67.1-1993.586999.99999999093278042.250000053278042.25000005951.009113412845.7408865872

67.2-199283999.99999999093296064.000000053296064.00000005951.876635221544.1233647785

67.3-1990.580999.99999999123314090.250000053314090.25000005952.741073793342.5089262067

67.4-198977999.99999999093332121.000000053332121.00000005953.6024542540.89754575

67.5-1987.574999.99999999123350156.250000053350156.25000005954.460801380539.2891986195

67.6-198671999.99999999093368196.000000053368196.00000005955.316139646437.6838603536

67.7-1984.568999.99999999093386240.250000053386240.25000005956.168493188436.0815068116

67.8-198365999.99999999123404289.000000053404289.00000005957.017885832134.4821141679

67.9-1981.562999.99999999093422342.250000053422342.25000005957.864341093432.8856589066

68-198059999.99999999123440400.000000053440400.00000005958.707882184531.2921178155

68.1-1978.556999.99999999093458462.250000053458462.25000005959.54853201929.701467981

68.2-197753999.99999999093476529.000000053476529.00000005960.386313217528.1136867825

68.3-1975.550999.99999999123494600.250000053494600.25000005961.221248112726.5287518873

68.4-197447999.99999999093512676.000000053512676.00000005962.053358754424.9466412456

68.5-1972.544999.99999999123530756.250000053530756.25000005962.882666914623.3673330854

68.6-197141999.99999999093548841.000000053548841.00000005963.709194092221.7908059078

68.7-1969.538999.99999999093566930.250000053566930.25000005964.532961518220.2170384818

68.8-196835999.99999999123585024.000000053585024.00000005965.353990159618.6460098404

68.9-1966.532999.99999999093603122.250000053603122.25000005966.172300724617.0776992754

69-196529999.99999999123621225.000000053621225.00000005966.987913666815.5120863332

69.1-1963.526999.99999999093639332.250000053639332.25000005967.800849189413.9491508106

69.2-196223999.99999999093657444.000000053657444.00000005968.611127249712.3888727503

69.3-1960.520999.99999999123675560.250000053675560.25000005969.418767563310.8312324367

69.4-195917999.99999999093693681.000000053693681.00000005970.22378960779.2762103923

69.5-1957.514999.99999999123711806.250000053711806.25000005971.02621262697.7237873731

69.6-195611999.99999999093729936.000000053729936.00000005971.82605563496.173944365

69.7-1954.58999.99999999093748070.250000053748070.25000005972.62333741974.6266625803

69.8-19535999.99999999123766209.000000053766209.00000005973.41807654673.0819234533

69.9-1951.52999.99999999093784352.250000053784352.25000005974.21029136291.5397086371

70-1950-0.00000000883802500.000000053802500.00000005975

70.1-1948.5-3000.00000000933820652.250000063820652.25000006975.7872203783

70.2-1947-6000.00000000933838809.000000063838809.00000006976.5719702096

70.3-1945.5-9000.00000000883856970.250000053856970.25000005977.3542670012

70.4-1944-12000.00000000933875136.000000063875136.00000006978.1341280586

70.5-1942.5-15000.00000000883893306.250000053893306.25000005978.9115704892

70.6-1941-18000.00000000933911481.000000063911481.00000006979.6866112051

70.7-1939.5-21000.00000000883929660.250000053929660.25000005980.4592669264

70.8-1938-24000.00000000883947844.000000053947844.00000005981.2295541842

70.9-1936.5-27000.00000000933966032.250000063966032.25000006981.9974893233

71-1935-30000.00000000883984225.000000053984225.00000005982.7630885057

71.1-1933.5-33000.00000000934002422.250000064002422.25000006983.5263677128

71.2-1932-36000.00000000884020624.000000054020624.00000005984.2873427486

71.3-1930.5-39000.00000000884038830.250000054038830.25000005985.0460292424

71.4-1929-42000.00000000934057041.000000064057041.00000006985.8024426512

71.5-1927.5-45000.00000000884075256.250000054075256.25000005986.5565982627

71.6-1926-48000.00000000934093476.000000064093476.00000006987.3085111977

71.7-1924.5-51000.00000000884111700.250000054111700.25000005988.0581964125

71.8-1923-54000.00000000884129929.000000054129929.00000005988.8056687018

71.9-1921.5-57000.00000000934148162.250000064148162.25000006989.5509427006

72-1920-60000.00000000884166400.000000054166400.00000005990.2940328869

72.1-1918.5-63000.00000000934184642.250000064184642.25000006991.0349535842

72.2-1917-66000.00000000884202889.000000054202889.00000005991.7737189633

72.3-1915.5-69000.00000000884221140.250000054221140.25000005992.510343045

72.4-1914-72000.00000000934239396.000000064239396.00000006993.2448397022

72.5-1912.5-75000.00000000884257656.250000054257656.25000005993.9772226617

72.6-1911-78000.00000000934275921.000000064275921.00000006994.707505507

72.7-1909.5-81000.00000000884294190.250000054294190.25000005995.4357016798

72.8-1908-84000.00000000884312464.000000054312464.00000005996.1618244825

72.9-1906.5-87000.00000000934330742.250000064330742.25000006996.8858870797

73-1905-90000.00000000884349025.000000054349025.00000005997.6079025008

73.1-1903.5-93000.00000001214367312.250000074367312.25000007998.3278836412

73.2-1902-96000.00000001214385604.000000074385604.00000007999.0458432649

73.3-1900.5-99000.00000001214403900.250000074403900.25000007999.7617940061

73.4-1899-102000.0000000124422201.000000074422201.000000071000.4757483708

Sheet1

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Prob. arrest

# Criminals

Sheet2

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B(166; 0.6)

A(0; 1.0)

D

EC

PP

# criminals

prob. Conviction

Sheet3

48.4-2274648000-129240568.5568.50.0010.73333206671100000

48.5-2272.56450004256.254256.25584.4349854384551.8150145616100.7208080808110

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Prob. arrest

# Criminals

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# criminals

prob. Arrestment

Simulation10-generation overlapping model (every agent lives for 10 periods)to maintain the diversity of agentsto simulate entry and exit50% of agents are honestdichotomous property from Fenders frameworkVarious initial probability of conviction and initial crime rateVarious size of society (number of agents)

Agents InteractionsWork, get paidCommit crimePay tax$500fail$2000succeedRecord the number of criminals and arrest them; collect tax from each workerGenerate new honest agents with income = $2000Work, get paid, pay taxPotential offendersHonest AgentsGenerate new potential offenderswith income {1000, , 3000}Retrieve info: last punishment rate and number of criminalsEvaluate the gain from crime and from work. Make decisions.Government

Preliminary ResultsInitial crime rate = 0%Various size of society (number of agents)The smaller the society, the higher the chance to reach zero crime rate equilibriumThe larger the society, the better the theoretical analysis in predicting the equilibrium outcome

Chart2

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probability to zero crime (%)

Analysis

Init Conv1000 agent2000 agent4000 agent8000 agent10000 agent56.1860.2363.356565.33

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Preliminary ResultsVarious initial crime rateFix size of society (number of agents)The higher the initial crime rate, the lower the chance to reach zero crime rate equilibriumAs initial crime rate becomes higher, the chance of zero crime equilibrium will be zero!

Chart3

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Initial Crime rate = 0.05

Init. Crime rate = 0.35

Initial Crime rate = 0.20

P=0.1

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Initial Prob. of Conviction (%)

Prob. of zero crime equilibrium (%)

Analysis2

P=0.1P=0.2P=0.3P=0.4P=0.5P=0.6P=0.7P=0.8

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ConclusionsCompared to a large society:Small society has higher advantage in reducing crime to zero crime rate equilibriumSmall society has lower advantage in utilizing theoretical analysis Low initial crime rate is essential to reach zero crime rate equilibriumIf a social disturbance causes the crime rate to increase to a high level, then zero crime rate equilibrium will not ever appear.

Free Market Control for a Multi-Agent Based Peer Help Environment

Kevin Kostiuk and Julita Vassileva

Context: I-Help a MAS for peer help

I-Help can be viewed as a special case of electronic marketThere are users who possess some goods or resources (knowledge in this case) and users who need these goods / resources (asking for help or advice);A person interested to buy a good must find a seller who offers the good with acceptable quality and at acceptable price. In I-Help a user with a specific help request needs to find a competent helper;The buyer is willing to pay some amount of money in order to achieve the goal of gaining some knowledge, while the seller is willing to give away some knowledge in exchange for money. The goal of accumulating some resource, like money, which can be exchanged with some goods (promotion, salary increase in workplace environment, marks in University environment) creates a motivation for knowledgeable users / students to participate.The price of a certain good depends on the offer and the demand for this good on the market. People having exceptional and highly demanded knowledge / expertise, can put higher prices for their advice.There is some cost associated with supplying the buyer with the good; helping costs some time for the helper, which could be used for achieving some other goal.

Designing an economyAn exercise in designing emergent controlFree-market model chosen:Allows to seek equilibrium quickly and provides access to control parameters.in the absence of externalities, competitive equilibria correspond to efficient resource allocations.Practice demonstrates that fiscal, monetary, and trade policy can guide macroeconomic behaviour, while taxation and redistribution can support individual agent welfare.Free-market mechanisms are extendible because they provide a foundation for other economic models.

Specifics of the problemThe "wellness" of the economy is not measured by the money turnover, but by the accumulated knowledge of the users.In conventional market the prices emerge and develop historically, electronic markets start usually with real-world prices. In our case, due to the unusual nature of the good "knowledge", there is no price history.We cant expect a purely rational behavior from the sellers and buyersThere are two levels of interaction in the environment: the "real world", where the players are users / students, and an "agent world where the players are the personal agents.

RequirementsEducational (Meta-level):Involve all students.Create general enthusiasm, but not divert from main goal (learning).Allow unclassified resources into the economy (e.g. tutorials, FAQ, tutors etc.).Require little maintenance.Obtain measurable results.

Requirements (cont.)Desired Macroeconomic Behaviours:Establish and maintain trade.Achieve a well-behaved price level.Realize net gains from trade (overall knowledge of students).Distribute benefits fairly.

Requirements (cont.)Agent Welfare Constraints:Encourage win-win transactions.Not make anyone worse off.The coordination mechanism must seek Pareto preferred states;Collectivist perspectives prefer the maximization of average or minimum welfare;However seeking collective welfare within a free-market structure threatens the participation of individual users, particularly the most productive and competent ones; only coercion ensures the participation of students made worse off by the system.Avoid unreasonable wealth accumulation.

Individual AgentUtility functionU = a(DM) + (1- a)(CDG)DM change in MoneyDG change in Knowledge greed for money 0

Expected outcomeStratification of societyProvider-type user (knowledgeable, helping, a -->1 ) will experience a significant increase in money balance with some increase in grade. Consumer-type user (little knowledge, cant help anyone, but needs help, a 0) will experience a significant decrease in money balance with some increase in grade.Dual-type user (both helps and asks for help, a 0.5) will experience a nearly constant money balance with a larger increase in grade.In the absence of a re-distribution mechanism, it appears that benefits will fall primarily to average and better than average students.

Economic InfrastructureCurrency that is visible to users and has external value.Tax system.Initial currency endowment.Well known and stable money supply.Posted trade prices and volumes.Policing.

So what happened?Tried it out in MA-version of I-Help (1 on 1)In winter 1999/2000Very little usageCurrency cashed in souvenirs not motivating; marks would be betterEnsuring presence the hardest problem

Result: I-Help 1 on 1 was abandoned entirely

And finally, Comtella! A P2P system for sharing papersThree versions:Comtella 1: 2002-2003 for sharing academic papers (files) among graduate students, Gnutella-based, fully distributed.Comtella 2: 2003/2004 for sharing class-related papers (URLs), physically centralized, used in CMPT 490 last winterComtella 3 which you are using right now

Comtella 1 Interface

Ensuring presenceP2P but on one machine!Comtella 1Comtella 2

Interface of Comtella 2

Ensuring New ContributionsIntroducing Status in Comtella 2

Persuasion strategy used in CRM Examples: Club memberships, Air Miles etc.Based on the theory of discrete emotions (fear) effective persuasion strategyStatus based on a combination of participation metricsvisualized as a card (gold, silver or bronze) - exampleHigh-status users are rewarded with Visibility in the community (visualization by status)Better search options for gold and silver members

Solution = introduce a notion of social status, combined with visualization and rewards

Ensuring New Contributions Rewarding Contributions

ExperimentsIs P2P useful in the classroom?Compare two offerings of the Ethics in CS class, in 2003 and 2004, one without and one with Comtella 2.Metrics: # papers brought in by students, regularit