AGENT-BASED COMPUTATIONAL ECONOMICS:AGENT-BASED COMPUTATIONAL ECONOMICS:APPLICATIONS TO ECONOMIC MODELLING, APPLICATIONS TO ECONOMIC MODELLING,

MARKET AND POLICY DESIGNMARKET AND POLICY DESIGN

Lecture 1 Slides Sheri M. MarkoseLecture 1 Slides Sheri M. Markose

Economics Department and Centre For Economics Department and Centre For Computational Finance and Economic Agents Computational Finance and Economic Agents

(CCFEA)(CCFEA)

University of Essex, UKUniversity of Essex, UK. . scher@essex.ac.ukscher@essex.ac.uk

Talk Prepared for Prime Minister Talk Prepared for Prime Minister Strategy Unit: 28 Sept. 2005Strategy Unit: 28 Sept. 2005    

Road Map of TalkRoad Map of Talk Where has ACE come from ? Essex Centre Where has ACE come from ? Essex Centre

CCFEA’s pioneering roleCCFEA’s pioneering role What are its foundations ?What are its foundations ? What sort of applications has ACE resulted in What sort of applications has ACE resulted in ACE models at CCFEA: The Artificial Stock Market ACE models at CCFEA: The Artificial Stock Market

model and Herding; Interbank Large Value model and Herding; Interbank Large Value Payments : Project with Bank of England; Payments : Project with Bank of England; Endogenous Risk : Collapse of Currency Peg – Endogenous Risk : Collapse of Currency Peg – Black Wednesday; Cap and Trade -Design of Black Wednesday; Cap and Trade -Design of Smart Market for Congestion : Foresight Project Smart Market for Congestion : Foresight Project

DemosDemos Calibration and Real Time Analysis : Future Calibration and Real Time Analysis : Future

ChallengesChallenges Concluding remarksConcluding remarks

AGENT BASED COMPUTATIONAL ECONOMICS (ACE) : A AGENT BASED COMPUTATIONAL ECONOMICS (ACE) : A NEW,EXCITING SUB-FIELD OF ECONOMICSNEW,EXCITING SUB-FIELD OF ECONOMICS

THIS IS BASED ON THE NEW AGE WORLD OF ARTIFICIAL THIS IS BASED ON THE NEW AGE WORLD OF ARTIFICIAL ENVIRONMENTS WITHIN COMPUTERSENVIRONMENTS WITHIN COMPUTERS

ENTIRE SYSTEMS CAN BE RECREATED AND CAN THEN ENTIRE SYSTEMS CAN BE RECREATED AND CAN THEN DYNAMICALLY EVOLVE AND GROW : SOMETIMES CALLED DYNAMICALLY EVOLVE AND GROW : SOMETIMES CALLED ARTIFICIAL LIFEARTIFICIAL LIFE

THESE ENVIRONMENTS MAY HAVE PURELY ARITIFICIAL AGENTS THESE ENVIRONMENTS MAY HAVE PURELY ARITIFICIAL AGENTS AND/OR CAN HAVE INTERFACES WITH HUMANSAND/OR CAN HAVE INTERFACES WITH HUMANS

EXAMPLES OF THIS AT A HIGH LEVEL OF SOPHISTICATION ARE EXAMPLES OF THIS AT A HIGH LEVEL OF SOPHISTICATION ARE COMPUTER GAMESCOMPUTER GAMES

E-bay and use of ‘BOTs’ in Search Engines for lowest priceE-bay and use of ‘BOTs’ in Search Engines for lowest price

THIS NEW TECHNOLOGY IS INCREASINGLY BEING ADOPTED FOR THIS NEW TECHNOLOGY IS INCREASINGLY BEING ADOPTED FOR ECONOMIC MODELLING, MARKET AND POLICY DESIGNECONOMIC MODELLING, MARKET AND POLICY DESIGN

CCFEA: CENTRE AT ESSEX CCFEA: CENTRE AT ESSEX FIRST IN UK TO DEVELOP POSTGRADUATE CURRICULA IN ACEFIRST IN UK TO DEVELOP POSTGRADUATE CURRICULA IN ACE

THE CORE MODULE IN CCFEA MSc in Computational Agent THE CORE MODULE IN CCFEA MSc in Computational Agent Based Networks and E-Markets For Which we are gaining Based Networks and E-Markets For Which we are gaining recognition is recognition is CF902 :Computational Models of Agent CF902 :Computational Models of Agent Networks, Markets and Self-Organization Networks, Markets and Self-Organization

This June 13-15 2005 , CCFEA hosted the 10 This June 13-15 2005 , CCFEA hosted the 10 Anniversary of WEHIA, the pre-eminent Interacting Anniversary of WEHIA, the pre-eminent Interacting Economic Agents ConferenceEconomic Agents Conference

http://www.essex.ac.uk/wehia05/http://www.essex.ac.uk/wehia05/

Economists in the UK have been somewhat under Economists in the UK have been somewhat under the cosh with bureaucracy dictating what is ‘good’ the cosh with bureaucracy dictating what is ‘good’ science . The RAE has effectively prevented many science . The RAE has effectively prevented many from taking risks, innovating and going beyond the from taking risks, innovating and going beyond the traditional and mainstream. Hence, it is historic that traditional and mainstream. Hence, it is historic that WEHIA2005 was the first Economic Agents WEHIA2005 was the first Economic Agents conference that was held in the UK.conference that was held in the UK.

Thus, this new subfield of Thus, this new subfield of Economics uses the artificial Economics uses the artificial environment of agent modelling environment of agent modelling to understand phenomena that to understand phenomena that are anomalous in deductive are anomalous in deductive models of traditional Economics. models of traditional Economics.

Increasingly it is felt, the so Increasingly it is felt, the so called complexity sciences which called complexity sciences which is interdisciplinary in scope is the is interdisciplinary in scope is the way forward not only to way forward not only to understand socio-economic understand socio-economic systems but also to pragmatically systems but also to pragmatically intervene and design intervene and design institutional change. institutional change.

Interdisciplinary

Edifice of Complexity

Studies:

Physics,Biology and

Evolution,Population,

Society and Markets

Advances in Maths of Computation: Godel-Turing-Post

The ACE Revolution:Foundations

Advances in Computer Hardware and Software leading to artificial worlds

I.Markets As Complex AdaptiveSystems (CAS)Absence of Command andControl vs. Self OrganizationDynamics from largeNumbers of interacting agentsExamples:Innovation;Cities and transportSocial and Economic Networks,Stock market phenomena

Application of ACE

II:Market DesignExamples Trading Platform, Cap and Trade SystemsPollution MarketsCongestion MarketsComputational Testbedding and wind tunnel testsCombine with human experiments**Experiments in real time

III.Policy DesignAvoids Lucas Critique of Econometric modelsCheck out unintended consequences of bad policy design**ExperimentsIn real time

ACE in Box I applications overcome the limitations of ACE in Box I applications overcome the limitations of deductive methodology of traditional Economic deductive methodology of traditional Economic Analysis.Analysis.

That is problems that are NP-hard or non-computable can only That is problems that are NP-hard or non-computable can only self- organize as a result of agent interaction. Shyam self- organize as a result of agent interaction. Shyam Sunder is famous for having discovered the irrelevance of Sunder is famous for having discovered the irrelevance of intelligence in achieving efficiency at a collective level in intelligence in achieving efficiency at a collective level in some widely used auction markets. some widely used auction markets.

The outcomes cannot be brought about by command and The outcomes cannot be brought about by command and control. Studies on how the entire structure of socio-control. Studies on how the entire structure of socio-economic networks arise are also best understood using economic networks arise are also best understood using ACE. Eg. Jing Yang (BOE)/CCFEA analyse interbank network ACE. Eg. Jing Yang (BOE)/CCFEA analyse interbank network structure to understand their implications for the structure to understand their implications for the endogenous generation of systemic risk. endogenous generation of systemic risk.

ACE in Boxes II and III Covers the fundamental pragmatic ACE in Boxes II and III Covers the fundamental pragmatic aspects of economic agent models in the new field of aspects of economic agent models in the new field of Computational Mechanism Design Computational Mechanism Design

In pre internet economy – markets were a given. In the post In pre internet economy – markets were a given. In the post

internet era the design and implementation of markets/ internet era the design and implementation of markets/ trading platforms is in the purview of all. John Ledyard who trading platforms is in the purview of all. John Ledyard who is famous for designing the markets for pollution rights is a is famous for designing the markets for pollution rights is a pioneer in this. New future of micro-economicspioneer in this. New future of micro-economics

Who or what are Agents in Who or what are Agents in ACE?ACE?

Agents are computer programs with varying degrees Agents are computer programs with varying degrees of autonomy and/or computational intelligenceof autonomy and/or computational intelligence

Agents can have fixed simple decision rules or have Agents can have fixed simple decision rules or have full powers of adaptive algorithms with capacity of full powers of adaptive algorithms with capacity of self-referential calculationself-referential calculation

In traditional economics agents are assumed to have In traditional economics agents are assumed to have full rationality and that efficiency of the system as full rationality and that efficiency of the system as whole is meant to reflect this. This could be a whole is meant to reflect this. This could be a mistaken viewmistaken view

Example is Greek World Cup football team vs. Example is Greek World Cup football team vs. English Football team. Greek case:Each player is not English Football team. Greek case:Each player is not star but team wins championship; English case: star but team wins championship; English case: Each player is a star but team plays like an idiot !! Each player is a star but team plays like an idiot !! Ants and bees– each ant is stupid but ant/bee colony Ants and bees– each ant is stupid but ant/bee colony is amazing.is amazing.

What are agent-based What are agent-based simulations?simulations?

Using a model to replicate Using a model to replicate alternative realitiesalternative realities

Agent-based modelling has three Agent-based modelling has three characteristics:characteristics:

1.1. HeterogeneityHeterogeneity

2.2. Strategies: Fixed or AdaptiveStrategies: Fixed or Adaptive

3.3. Adaptive learningAdaptive learning

Agent based vs. Analytical Agent based vs. Analytical modelsmodels

Analytical models make simplifying assumptions: The Analytical models make simplifying assumptions: The representative agent ; equal size banks with equal size representative agent ; equal size banks with equal size paymentspayments

Agent based models can process and run data in real Agent based models can process and run data in real time and can simulate a system in time and can simulate a system in “model vérit锓model vérité” to to replicate its structural features and perform “wind replicate its structural features and perform “wind tunnel” tests. Eg. Of Interbank large value payments tunnel” tests. Eg. Of Interbank large value payments Simulator : ¼ of a country’s GNP goes through the Simulator : ¼ of a country’s GNP goes through the system system daily.daily.

Nirvana of Agent based Computational Economics (ACE)Nirvana of Agent based Computational Economics (ACE)– Have agents respond autonomously and strategically Have agents respond autonomously and strategically

to policy changesto policy changes

Example of An Agent Based Economic Example of An Agent Based Economic Model: Canonical Example of Self-Reflexive Model: Canonical Example of Self-Reflexive

Systems and Contrarian StructuresSystems and Contrarian Structures First example developed by Santa Fe Institute is the Artificial Stock Market (ASM)First example developed by Santa Fe Institute is the Artificial Stock Market (ASM)

Brian Arthur gave a powerful rebuttal of why traditional economic analysis will fail to Brian Arthur gave a powerful rebuttal of why traditional economic analysis will fail to understand stock markets and why ACE modelling is neededunderstand stock markets and why ACE modelling is needed

In a stock market an investor makes money if he/she can sell when everybody else is In a stock market an investor makes money if he/she can sell when everybody else is buying and buy when everybody else is selling. In other words, one needs to be in the buying and buy when everybody else is selling. In other words, one needs to be in the minority or contrarianminority or contrarian

Arthur called this the El Farol Bar problem. You want to go to the pub when it is not Arthur called this the El Farol Bar problem. You want to go to the pub when it is not crowded. Assume everybody else wants to do the same. How can you rationally crowded. Assume everybody else wants to do the same. How can you rationally decide/strategize to succeed in this objective of being in the minority ?decide/strategize to succeed in this objective of being in the minority ?

If all of us have the same forecasting model to work out how many people will turn up – If all of us have the same forecasting model to work out how many people will turn up – say our model says it will be 80% full – then as all of us do not want to be there when it say our model says it will be 80% full – then as all of us do not want to be there when it is crowded – none of us will go.is crowded – none of us will go.

This contradicts the prediction of our model and in fact we should go. If all reasoned This contradicts the prediction of our model and in fact we should go. If all reasoned this way – once again we will fail etc. So there is no this way – once again we will fail etc. So there is no Homogenous Rational Homogenous Rational ExpectationsExpectations and no rational way in which we can decide to go. Traditional economics and no rational way in which we can decide to go. Traditional economics cannot deal with thiscannot deal with this

Hence, Brian Arthur said we must use ACE models and see how the system dynamically Hence, Brian Arthur said we must use ACE models and see how the system dynamically self-organizes self-organizes

Simple Stock Market Model WithSimple Stock Market Model WithAgents Relying on Investment ‘Tips’ Agents Relying on Investment ‘Tips’

From OthersFrom Others

http://privatewww.essex.ac.uk/~aalent/herding/herding.htmhttp://privatewww.essex.ac.uk/~aalent/herding/herding.htm Agents have to buy or sell one unit of an asset; they take advise from Agents have to buy or sell one unit of an asset; they take advise from

their neighbours; they act on the basis of the majority view amongst their neighbours; they act on the basis of the majority view amongst their neighbours;their neighbours;

Neighbours who give bad advise are eventually cut off and new Neighbours who give bad advise are eventually cut off and new advisers are foundadvisers are found

All agents are identical except for how far back they can remember;All agents are identical except for how far back they can remember;Some have zero memory and they give random advise; others with Some have zero memory and they give random advise; others with

memory give the average trend of the marketmemory give the average trend of the marketWho will give best advise in a minority winning structure ?Who will give best advise in a minority winning structure ?Eventually what does the communication network look like?Eventually what does the communication network look like? Hence, paper is called Dynamic Learning, Herding and Guru EffectsHence, paper is called Dynamic Learning, Herding and Guru Effects

Features of Herding Features of Herding SimulatorSimulator The aim is to model a network that has the The aim is to model a network that has the

properties of a properties of a real world networkreal world network

The main feature of real world networks is: The main feature of real world networks is: - High clustering coefficient (Internet example)- High clustering coefficient (Internet example)- Star formationsStar formations

The paper contrasts clustering which represents The paper contrasts clustering which represents the network topology of the underlying the network topology of the underlying communication network with herding which communication network with herding which represents aggregate behaviour with regard to a represents aggregate behaviour with regard to a binary decision problem.binary decision problem.

Starting from a random graph we study how Starting from a random graph we study how star star formationsformations can take place by dynamically updating the can take place by dynamically updating the links. This type of study would be very difficult to carry links. This type of study would be very difficult to carry out with traditional economic models. out with traditional economic models. Kirman (1997), Kirman (1997), Kirman and Vignes (1991) suggest dynamic link Kirman and Vignes (1991) suggest dynamic link formation: reinforced by good experience and broken formation: reinforced by good experience and broken by bad ones. by bad ones.

Peyton Young, one of the pioneers of network Peyton Young, one of the pioneers of network economics has now introduced the notion of economics has now introduced the notion of radical radical decouplingdecoupling . Unlike, traditional games where agents . Unlike, traditional games where agents know the rules of the game, here and in most real know the rules of the game, here and in most real world situations, one can learn to win only by having world situations, one can learn to win only by having the ‘right’ connections or advisors. the ‘right’ connections or advisors.

Properties of NetworksProperties of NetworksDiagonal Elements Characterize Small World NetworksDiagonal Elements Characterize Small World Networks

Watts and Strogatz (1998), Watts (2002)Watts and Strogatz (1998), Watts (2002) Properties Networks

Clustering Coefficient

Average Path Length

Degree Distribution

Regular

High

High

Equal and fixed In-degrees to each node

Random

Low

Low

Exponential/ Poisson

Scale Free/Power Law

Low

Variable

Fat Tail Distribution

Dynamic Updating of Links Dynamic Updating of Links

The weights wThe weights wijij to the neighbours who give to the neighbours who give correct advise are reinforced by a correct advise are reinforced by a rate of rate of incrementincrement R Rii

++, up to a maximum threshold , up to a maximum threshold ΓΓ maxmax

And weights to neighbours who give incorrect And weights to neighbours who give incorrect advise are reduced by a advise are reduced by a rate of reductionrate of reduction R Rrr

--

There is a There is a Minimum thresholdMinimum threshold ΓΓminmin, after which , after which the agent breaks the link to the neighbour, the agent breaks the link to the neighbour, and and randomly selectsrandomly selects another agent in the another agent in the network to take advice from.network to take advice from.

Clustering coefficientClustering coefficient Clustering coefficient:Clustering coefficient: average probability that two average probability that two

neighbours of a given node (agent) are also neighbours of a given node (agent) are also neighbours of one another. neighbours of one another. The clustering The clustering coefficient Ci for agent i is given by:coefficient Ci for agent i is given by:

Ci = )1( ii

i

kk

E

The clustering coefficient of the network as a whole The clustering coefficient of the network as a whole

is the average of all Ci’s and is given byis the average of all Ci’s and is given by

C= N

CN

ii

1

Ei = i ij m

jma 1

; C; Crandrand = p = p

Herding coefficientHerding coefficientThe herding phenomenon in both classes of experiments is captured at each t by a

time varying simple herding function N

Nbt [0, 1]. Here, Nbt is the number of

agents who have bought at time t and N is the total number of agents.

The average measure of herding in the system over the length of time T which is

irrespective of the direction of herding is given by a herding coefficient

T

tbt

NN

TN

1

2

2

1/2 , (0, 1).

Results:Highly connected Results:Highly connected agentsagents

We find that agents with We find that agents with zero-memoryzero-memory become highly connected.become highly connected.

Why? Because playing the Minority game in Why? Because playing the Minority game in isolation, zero-memory agents perform isolation, zero-memory agents perform best, while other agents become trend-best, while other agents become trend-followers. followers.

These highly connected nodes can be seen These highly connected nodes can be seen as “gurus”:as “gurus”:– Many agents take advice from themMany agents take advice from them

Degree distributionsDegree distributions

Degree distribution Degree distribution of the initial of the initial

random networkrandom network

(a)

(b) Degree distributionDegree distribution

of the network after of the network after the dynamic the dynamic

updating of linksupdating of links

A graphical representationA graphical representation

Rates of adjustmentRates of adjustment We find that a necessary condition for the We find that a necessary condition for the

agents to find the “gurus” is that: Ragents to find the “gurus” is that: Rrr > R > Rii

But too much inertia (Rr >>) cause But too much inertia (Rr >>) cause instabilityinstability

Fixed Rate of Increment = +0.2

0

0.1

0.2

0.3

0.4

0.5

0.6

-1 -0.8 -0.6 -0.4 -0.2 0

Rate of reduction

Clu

ste

rin

g C

oeff

icie

nt

Influence of gurus on Influence of gurus on herdingherding

Dynamic Learning in Minority Game : Herding With Clustering C= 0.57Dynamic Learning in Minority Game : Herding With Clustering C= 0.57 ( p= 0.2; R- =-0.4, R+ =0.2 ;T= 1000)( p= 0.2; R- =-0.4, R+ =0.2 ;T= 1000)

Dynamic Learning in Minority Game : Herding With Clustering C= 0.84 Dynamic Learning in Minority Game : Herding With Clustering C= 0.84

(p= 0.1; R- =-0.4, R+ =0.2 ;T= 1000)(p= 0.1; R- =-0.4, R+ =0.2 ;T= 1000)

4. Conclusions of Herding 4. Conclusions of Herding SimulatorSimulator

Agents discover the gurus in the system, by Agents discover the gurus in the system, by simple adaptive threshold behaviour and random simple adaptive threshold behaviour and random sampling.sampling.

The dynamic process of link formation produces The dynamic process of link formation produces the star/hub formations in the network topology the star/hub formations in the network topology often found in real world networksoften found in real world networks..

When updating the links, the rate of reduction has When updating the links, the rate of reduction has to be greater than the rate of increment.to be greater than the rate of increment.

We succeed in producing small world network We succeed in producing small world network properties of C>Cproperties of C>Crandrand and shorter average path and shorter average path length than random graphs.length than random graphs.

Other ACE Model at CCFEA: Other ACE Model at CCFEA: Policy DesignPolicy Design

Real time interbank payment gameReal time interbank payment game Modelled for the Bank of England to Modelled for the Bank of England to

understand how a new set of policy understand how a new set of policy rules may farerules may fare

http://privatewww.essex.ac.uk/~aalent/IPSS/http://privatewww.essex.ac.uk/~aalent/IPSS/

Designing Large Value Designing Large Value Payment Systems: An Agent-Payment Systems: An Agent-

based approachbased approach

Amadeo Alentorn Amadeo Alentorn CCFEA, University of Essex CCFEA, University of Essex

Sheri Markose Sheri Markose Economics/CCFEA, University of Economics/CCFEA, University of EssexEssex

Stephen Millard Stephen Millard Bank of EnglandBank of England Jing Yang Jing Yang Bank of EnglandBank of England

Expert Forum: Payment System Architecture and Oversight - 1st Feb 2005

What are the design issues in a What are the design issues in a LVPS?LVPS?

Three objectives :Three objectives :

1.1. Reduction of settlement Reduction of settlement riskrisk2.2. ImprovingImproving efficiency efficiency of liquidity usage : of liquidity usage :

¼ of a country’s GNP goes through the ¼ of a country’s GNP goes through the interbank system on a interbank system on a daily daily basisbasis

3.3. Improving settlementImproving settlement speed speed (operational risk)(operational risk)

Design issuesDesign issues

Two polar extremes:Two polar extremes:- Deferred Net Settlement (Deferred Net Settlement (DNS)DNS)- Real Time Gross Settlement (RTGS) Real Time Gross Settlement (RTGS)

LiquiditLiquidityy

DelayDelay

DNSDNS LowLow HighHigh

RTGSRTGS HighHigh LowLow Hybrids+

Example: DNS vs. RTGSExample: DNS vs. RTGS

10£10£

10£ 10£

Bank C

Bank B

Bank A

LiquidityLiquidity

DNSDNS 0 £0 £

RTGSRTGS 40 £40 £Bank

D

Logistics of liquidity postingLogistics of liquidity posting Intraday liquidity can be obtained in two ways: waiting for Intraday liquidity can be obtained in two ways: waiting for

incoming paymentsincoming payments; or ; or posting liquidityposting liquidity. .

Two ways of posting liquidity in RTGS:Two ways of posting liquidity in RTGS:– Just in Time (JIT): Just in Time (JIT): raise liquidity whenever needed paying a fee raise liquidity whenever needed paying a fee

to a central bank, like in to a central bank, like in FedWire USFedWire US– Open Liquidity (OL): Open Liquidity (OL): obtain liquidity at the beginning of the day obtain liquidity at the beginning of the day

by posting collateralby posting collateral, like in CHAPS UK, like in CHAPS UK

A good payment system should encourage participants to A good payment system should encourage participants to efficiently recycle the liquidity in the system. efficiently recycle the liquidity in the system.

Folk theorem: “A dollar posted earlier in the day improves the Folk theorem: “A dollar posted earlier in the day improves the liquidity recycling capabilities of RTGS”liquidity recycling capabilities of RTGS”

Risk-efficiency trade off (I)Risk-efficiency trade off (I)

RTGS avoids the situation where the failure of RTGS avoids the situation where the failure of one bank may cause the failure of others due to one bank may cause the failure of others due to the exposures accumulated throughout a day;the exposures accumulated throughout a day;

However, this reduction of settlement However, this reduction of settlement riskrisk comes at a comes at a costcost of increased intraday liquidity of increased intraday liquidity needed to smooth the non-synchronized needed to smooth the non-synchronized payment flows.payment flows.

Risk-efficiency trade off (II)Risk-efficiency trade off (II) Free Riding Problem:Free Riding Problem:

– Nash equilibrium Nash equilibrium àà la Prisoner's Dilemma, where non- la Prisoner's Dilemma, where non-cooperation is the dominant strategycooperation is the dominant strategy

If liquidity is costly, but there are no delay costs, it is optimal at If liquidity is costly, but there are no delay costs, it is optimal at the individual bank level to delay until the end of the day.the individual bank level to delay until the end of the day.

Free riding implies that no bank voluntarily post liquidity and Free riding implies that no bank voluntarily post liquidity and one waits for incoming payments. All banks may only make one waits for incoming payments. All banks may only make payments with high priority costs.payments with high priority costs.

So So hidden queueshidden queues and gridlock occur, which can compromise and gridlock occur, which can compromise the integrity of RTGS settlement capabilities.the integrity of RTGS settlement capabilities.

What can IPSS do?What can IPSS do? http://privatewww.essex.ac.uk/~aalent/IPSS/IPSS_2_10.exehttp://privatewww.essex.ac.uk/~aalent/IPSS/IPSS_2_10.exe

Interbank structureInterbank structure

Heterogeneous banks in terms of their Heterogeneous banks in terms of their size of payments and market share size of payments and market share

--tiering N+1;tiering N+1;

-impact of -impact of participation structureparticipation structure on risks. on risks.

Herfindahl IndexHerfindahl Index measures the concentration of payment measures the concentration of payment

activity:activity:

In general, the Herfindahl Index will lie In general, the Herfindahl Index will lie between 0.5 and 1/between 0.5 and 1/nn, where , where nn is the is the number of banks. number of banks.

It will equal 1/It will equal 1/nn when payment activity is when payment activity is equally divided between the equally divided between the nn banks. banks.

HIPayments =

i

i

PaymentsofValueTotal

PaymentsBank2

Herfindahl Index Asymmetry Herfindahl Index Asymmetry And Liquidity NeedsAnd Liquidity Needs

Bilateral DNSBilateral DNS Lower BoundLower Bound(Multilateral DNS)(Multilateral DNS)

Upper Upper BoundBound

Equal Size BanksEqual Size Banks(Proxied Data ) (Proxied Data ) Herfindhal IndexHerfindhal Index 1/14 ~ 0.0711/14 ~ 0.071

00

00

£2.4 bn £2.4 bn

Real Chaps DataReal Chaps DataHerfindhal Index ~ 0.2Herfindhal Index ~ 0.2

£ 19.6 bn£ 19.6 bn £5.6 bn£5.6 bn £22.2 bn£22.2 bn

Proxied Data (IID Real)Proxied Data (IID Real)Herfindahl Index ~ 0.2Herfindahl Index ~ 0.2

£19.6 bn£19.6 bn £5.6 bn£5.6 bn £17.6 bn£17.6 bn

Note that total value of payments is the same in all scenarios

Bank Failure analysisBank Failure analysis IPSS allows to simulate the failure of a bank, and to IPSS allows to simulate the failure of a bank, and to

observe the effects. For example, under JIT:observe the effects. For example, under JIT:

Note that, because of the asymmetry of the UK banking Note that, because of the asymmetry of the UK banking system, a failure of a bank would have a very different system, a failure of a bank would have a very different effect, depending on the size of the failed bank.effect, depending on the size of the failed bank.

ScenarioScenario Failure big Failure big bank (K)bank (K)

Failure small Failure small bank (F)bank (F)

Chaps IID RealChaps IID Real 32,38432,384

£94.2 bn£94.2 bn2,6342,634

£1.0 bn£1.0 bn

Equal size banksEqual size banks(with same total value of (with same total value of payments and arrival time)payments and arrival time)

11,73211,732

£21,1 bn£21,1 bn

Endogenous Risk : Lucas Endogenous Risk : Lucas CritiqueCritique

And Policy IneffectivenessAnd Policy Ineffectiveness CCFEA Modelling of a classic example of poor policy design leading to collapse of a CCFEA Modelling of a classic example of poor policy design leading to collapse of a system: Black Wednesday and Collapse of the ERM Currency peg on 19 Sept. 2002system: Black Wednesday and Collapse of the ERM Currency peg on 19 Sept. 2002

George Soros made £2bn taking a short position against the Sterling and the Bank of George Soros made £2bn taking a short position against the Sterling and the Bank of England. He is alleged to have used the Liar or Contrarian Strategy. England. He is alleged to have used the Liar or Contrarian Strategy. Why did Soros Why did Soros win : win : Or why didOr why did all all Currency pegs collapseCurrency pegs collapse (from Mexico to the Asian (from Mexico to the Asian ones) ones)

Soros cut above ordinary speculator: student of Karl Popper Soros cut above ordinary speculator: student of Karl Popper and knows the self-reflexive problem of the Cretan Liar. and knows the self-reflexive problem of the Cretan Liar. Liar can subvert only from a a point of certainty or Liar can subvert only from a a point of certainty or computable fixed point. Hence, if the policy position is computable fixed point. Hence, if the policy position is perfectly known – hostile agents can destroy it. perfectly known – hostile agents can destroy it. Indeterminism or ambiguity is a essential design element Indeterminism or ambiguity is a essential design element for success of market systems and zero sum gamesfor success of market systems and zero sum games

30 tests using a ABM wind tunnel test of the currency peg with a 30 tests using a ABM wind tunnel test of the currency peg with a central bank intervening to raise the exchange and speculator central bank intervening to raise the exchange and speculator taking a short position shows that the bank cannot win even once taking a short position shows that the bank cannot win even once viz. ran out of reservesviz. ran out of reserves

70

75

80

85

90

95

100

105

110

115

120

Jul-

92

Sep-

92

Nov

-92

Jan-

93

Mac

93

May

-93

Jul-

93

Sep-

93

Nov

-93

Jan-

94

Mac

94

May

-94

Jul-

94

Sep-

94

Nov

-94

Jan-

95

Mac

95

May

-95

Jul-

95

Belgium Denmark France UK

Exchange rates against the deutschemark for the Belgian franc, French franc, Danish krone

and British pound sterling July 1992-July 1995 (index).

Source: Eurostatistik, April 1993, 1994, 1996, August and October 1995

ERM Currency peg requires Central Banks to precommit and ERM Currency peg requires Central Banks to precommit and support the currency when the Forex rate falls below peg: support the currency when the Forex rate falls below peg:

Central Bank RuleCentral Bank Rule

Figure shows that the state of the fundamentals relating to the Figure shows that the state of the fundamentals relating to the long term viability of the parity was neither necessary nor long term viability of the parity was neither necessary nor sufficient for speculative attacks. U.K with a 20% overvalued sufficient for speculative attacks. U.K with a 20% overvalued currency sustained attacks as did the other ERM currencies whose currency sustained attacks as did the other ERM currencies whose parities appear to be virtually unchanged within the pegged parities appear to be virtually unchanged within the pegged regime and when it effectively floated. regime and when it effectively floated.

The only material difference in the case is with the widening of the The only material difference in the case is with the widening of the bands from bands from 2.5 % to 2.5 % to 15% was that it rendered the rule dead 15% was that it rendered the rule dead letter and when the conditions of a defence were made letter and when the conditions of a defence were made ambiguous, the speculative attacks ceased dramatically. ambiguous, the speculative attacks ceased dramatically.

Flawed Macro economic literature on precommitment to Flawed Macro economic literature on precommitment to transparent strategy caused IMF to support currency pegs and led transparent strategy caused IMF to support currency pegs and led to the worst policy induced failures of our timeto the worst policy induced failures of our time

What provokes the attacks is the transparent defence : Speculator What provokes the attacks is the transparent defence : Speculator Sells forward Sells forward afterafter the central bank raises the exchange rate to the central bank raises the exchange rate to above the lower bound : Speculator Rule above the lower bound : Speculator Rule

After the collapse at least Charles Goodhart said : After the collapse at least Charles Goodhart said : If at the first If at the first whiff of trouble the best response is to float : why peg ?whiff of trouble the best response is to float : why peg ?

Foregi n exchange reserves of UK , 1992

32000

34000

36000

38000

40000

42000

(millions ofU.S.dollar)

Souce:IMF,International Financial Statistics.

CAP and TRADE: Smart Market CAP and TRADE: Smart Market for Congestion – IIS Foresight for Congestion – IIS Foresight

ProjectProject Joint with Transport and Operations Research Group (TORG, Joint with Transport and Operations Research Group (TORG,

Newcastle) and Cranfield Centre for Complexity Studies (Peter Newcastle) and Cranfield Centre for Complexity Studies (Peter Allen)Allen)

SMPRT : Smart Market For Passenger Road TransportSMPRT : Smart Market For Passenger Road Transport

Based on a uniform sealed bid Dutch Auction Design where the KBased on a uniform sealed bid Dutch Auction Design where the K

Highest bid that clears the market for K travel slots applies to all Highest bid that clears the market for K travel slots applies to all bidders who bid above this. SMPRT algorithm also covers bidders who bid above this. SMPRT algorithm also covers externality costsexternality costs

Rationale (See over)Rationale (See over)

The traditional view is that economic development with its ever increasing demand for road transport in urban settings and the consumption of non-renewable energy sources with their respective consequences of congestion and pollution are but necessary evils that must be collectively borne. A program of economic development that fully prices and internalizes the externality costs that the private cost- benefit calculus cannot incorporate is seen as essential to prevent the overuse and degradation of resources. The latter is powerfully brought out in Garett Hardin’s classic paper on the Tragedy of the Commons where a decline in social welfare and total output occurs as there is no institution to signal and correct for the negative impact of private behaviour on society as a whole.

It is increasingly being understood, with the earliest and successful

implementation of a program for pollution control with the US EPA

(Environmental Protection Act), that the way to internalize and account for the

negative externalities of some economic activities is to use market solutions

rather than command and control type regulation.

The ‘cap’ and trade solution to the

problem of externalities is what underpins the SMPRT. Further, only from

bids submitted by motorists can we obtain information on private value

II. How to ‘cap’ road use constitutes the first part of the project The cap is based on the actual physical network characteristics of an area of road network which is identified as a congestion hot spot and is determined on the two respective sets of factors relating to the deterioration in traffic efficiency and the growth of environmental degradation from chemical and noise pollutants. These are gauged by a state of the art traffic micro simulator : embellished with all real time features of the cityscape: traffic lights, round abouts etc.

The cap determines the maximum number of road users permitted to use the road during a time slice in terms of the above criteria. The cap is given as K passenger car units (PCUs).

The GCV simulations done by TORG highlights The sensitivity of traffic efficiency, measured in total travel times and total distance covered, to congestion is clearly shown. Likewise, the rate of pollution given in terms of the different is sensitive to congestion. The deterioration of traffic conditions and the pollution emission shows marked non-linearities at critical points.The assumption in traditional analysis that there is constant cost of environmental pollution in terms of linear distance travelled by PCUs is wrong. The determination of the ‘cap’ is clearly self-evident from the TORG simulations of the GCV model. This is shown in Figure 1CAP 23,000 PCUsDemand 26, 000 PCUs

Robustness AnalysisWe will discuss further the

computational agent based modelling that can be used to test the robustness of the proposed ‘cap’ and trade auction design. In other words, the success of the market design on the ground should

not be based on a wing and a pray !

The particular responsibility of designers of the any auction based

market is to identify the specific pitfalls associated with the nature of market

that may result in systematic overbidding or underbidding relative to

agents’ true valuations.

In the case of identical multi-unit good auction where the supplier aims to dispose off a given supply of a good with no resale, as in this case of a ‘cap’ and

trade auction market, the market clearing price can be highly sensitive to excess demand conditions

relative to cap. We call this variable RCAP,

RCAP = N/K where N is the number of bidders and K is the cap.

With risk neutral agents, we have identified a systematic tendency to underbid relative to true

valueIndeed, the strategic Nash equilibrium bids when

Rcap= 1 is zero.It takes sufficient excess demand Rcap > 1.2

pressure for at least the marginal Kth. Bidder who determines the market price to be induced to bid his

true value or the Nash equilibrium value. This finding is crucial for the proposed SMPRT to deliver

the ‘goods’. That is, if the cap is set too high relative to the

demand conditions – the auction protocol will fail with agents systematically underbidding.

Why? The auction will raise far little revenue relative to the Competitive Equilibrium (CE) case viz. when all agents

bid their true values.

Detail price formation when RCAP values: 1.2, 1.6, t Fig. 21, Fig. 22, and Fig. 23.

Fig. 21 Price formation for RCAP = 1.2

Fig. 22 Price formation for RCAP = 1.6

Smart Market for Congestion - Robustness Analysis - Doc- Simulator to study the auction design for a congestion charge model

Concluding RemarksConcluding Remarks CCFEA and ACE is an exciting new field to CCFEA and ACE is an exciting new field to

get into !get into ! You need to combine programming and You need to combine programming and

Computer Science knowledge with Computer Science knowledge with Economics and FinanceEconomics and Finance

CCFEA is becoming the victim of its CCFEA is becoming the victim of its success – we have made two permanent success – we have made two permanent appointments to the centre – but is in appointments to the centre – but is in need of research support staff !need of research support staff !

The ACE method is still in its infancy – has The ACE method is still in its infancy – has a lot to offer and has a long way to go.a lot to offer and has a long way to go.