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Market Based Control of Complex Computational Systems. Nick Jennings nrj@ecs.soton.ac.uk. The Complex Systems Challenge. Building software that operates effectively in environments that: Have no centralised control Are highly interconnected Are in constant state of flux

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Market based control of complex computational systems

Market Based Control of Complex Computational Systems

Nick Jennings

nrj@ecs.soton.ac.uk


The complex systems challenge
The Complex Systems Challenge

Building software that operates effectively in environments that:

  • Have no centralised control

  • Are highly interconnected

  • Are in constant state of flux

  • Are highly unpredictable

  • Involve multiple, individually-motivated actors


The complex systems landscape

Pervasive

Computing

Autonomic

Computing

Peer-to-Peer

eCommerce

Semantic

Grid

Semantic

integration

OGSA uses WS

standards

“Brain meets

Brawn”

The Complex Systems Landscape

Web Services

Semantic Web

Service description

Service discovery

Service composition

Flexible interoperation &

reasoning in heterogeneous

environments

Agent Based Computing

Grid Computing

Robust, large scale

open systems

Autonomy

Rich interactions


The computational model

(Jennings, 2000 & 2001)

Electronic

institution

Organisational

relationship

Agent

Interaction

Environment

Sphere of

influence

The Computational Model

  • Entities offer services in an institutional setting

  • Entities connect to services

    • Service discovery

    • Service composition

    • Service procurement

  • Entities enact services

    • Flexible & context sensitive service delivery


Agents as service providers consumers

“encapsulated computer system, situated in some environment, and capable

of flexible autonomous action in that environment in order to

meet its objectives”

Agents as Service Providers & Consumers


Agents as service providers consumers1

“encapsulated computer system, situated in some environment, and capable

of flexible autonomous action in that environment in order to

meet its objectives”

Agents as Service Providers & Consumers

  • control over internal state and over own behaviour


Agents as service providers consumers2

“encapsulated computer system, environment, and capable situated in some environment, and capable

of flexible autonomous action in that environment in order to

meet its objectives”

Agents as Service Providers & Consumers

  • control over internal state and over own behaviour

  • experiences environment through sensors and acts through effectors


Agents as service providers consumers3

“encapsulated computer system, situated in some environment, and capable

of flexible autonomous action in that environment in order to

meet its objectives”

Agents as Service Providers & Consumers

  • control over internal state and over own behaviour

  • experiences environment through sensors and acts through effectors

  • reactive: respond in timely fashion to environmental change

  • proactive: act in anticipation of future goals


Negotiation as de facto form of interaction

Agree appropriate service contracts environment, and capable

Service composition

Service procurement

Fixed price offerings

Catalogues

Dynamic pricing

Negotiations

Auctions

Economic efficiency

Historical precedent

Negotiation as de facto Form of Interaction


Computational service economies

permissible participants environment, and capable

e.g. buyers, sellers & third parties

interaction states

e.g. accepting bids, auction closed

events causing state transitions

e.g. bid, time out, bid accepted

valid actions

bid, ask, propose, accept, reject,

counter-proposal, critique

reward structures

who pays & who gets paid for what

Computational Service Economies

(Dash et al., 2003)

Mechanism Design

“rules of the game”


Computational service economies1

shaped by interaction protocol environment, and capable

decision making employed to achieve trading objectives

from very simple to very complex

maximise benefit

to self (self interest) and/or

to group (social welfare)

permissible participants

e.g. buyers, sellers & third parties

interaction states

e.g. accepting bids, auction closed

events causing state transitions

e.g. bid, time out, bid accepted

valid actions

bid, ask, propose, accept, reject,

counter-proposal, critique

reward structures

who pays & who gets paid for what

Computational Service Economies

(Dash et al., 2003)

Mechanism Design

Agent Strategies

“rules of the game”

“how to succeed in the game”


Computational service economies2

shaped by interaction protocol environment, and capable

decision making employed to achieve trading objectives

from very simple to very complex

maximise benefit

to self (self interest) and/or

to group (social welfare)

permissible participants

e.g. buyers, sellers & third parties

interaction states

e.g. accepting bids, auction closed

events causing state transitions

e.g. bid, time out, bid accepted

valid actions

bid, ask, propose, accept, reject,

counter-proposal, critique

reward structures

who pays & who gets paid for what

Computational Service Economies

(Dash et al., 2003)

Mechanism Design

Agent Strategies

Game theory analyses interactions to determine likely outcomes and equilibria

“rules of the game”

“how to succeed in the game”


The market based control project
The Market-Based Control Project environment, and capable

  • Market-Based Control (MBC):

    • paradigm for controlling computer systems using economically-inspired techniques

  • Market mechanisms used to:

    • generate and predict emerging system properties,

      • although decisions are made independently by local agents that each have their own aims and objectives

    • a market is a self-organising system, directed by mechanism

  • The proposition:

    • MBC is good for effectively controlling and managing complex, adaptive, distributed computational systems


Objectives
Objectives environment, and capable

  • Develop and evaluate core MBC technologies

  • Automated mechanism design

    • Automate design of market mechanisms to achieve a desired set of global goals

    • Adapt to a changing environment and changing (priority of) objectives

    • Predict and automate design of agent strategies

  • Apply MBC solutions to design and manage complex, distributed computational systems


Project applications
Project Applications environment, and capable

  • Utility data centres

    • MBC to allocate computational resources & achieve a robust, scalable service

  • Distributed content delivery within p2p networks

    • MBC to regulate sharing of content

  • Decentralised control and scheduling of multiple robots

    • MBC to provide incentives for cooperation and to achieve global goals


Research highlights
Research Highlights environment, and capable

  • Competing sellers in online auctions

  • Strategies for bidding in multiple auctions

  • Empirical game theory to select mechanisms and strategies for complex markets

  • Adaptive auctions


Research highlights1
Research Highlights environment, and capable

  • Competing sellers in online auctions

  • Strategies for bidding in multiple auctions

  • Empirical game theory to select mechanisms and strategies for complex markets

  • Adaptive auctions


Market based control of complex computational systems


Market based control of complex computational systems

  • Often strong competition among sellers in online auctions environment, and capable

  • Seller’s choice of mechanism & auction parameters affect buyer’s choice of seller

    • How should bidder choose between auctions/sellers?

    • How should a seller set its parameters?

  • Focus on seller’s reserve price & sealed-bid auctions


Model of competing sellers
Model of Competing Sellers environment, and capable

  • Set & announce Reserve Price

Seller

Seller

Seller

Mediator

Auction

Auction

Auction

  • Set & announce Auction Fees

Buyers

  • Select seller

  • Bid in auctions


Shill bidding
Shill Bidding environment, and capable

  • Competing sellers reduces optimal reserve price and expected revenue (compared to isolated auctions)

  • Avoid by shill bidding:

    • Seller disguised as buyer to bid in own auction.

  • Illegal and undesired, but hard to detect

    • But mediator can use auction fees to deter it

  • Use Evolutionary Simulationto:

    • Evaluate effectiveness of different types of auction fees in deterring shill bidding

    • Measure market efficiency


Results with auction fees
Results with Auction Fees environment, and capable

Fraction of auctions won by shill bids

Allocative efficiency

CP= closing price

RD = difference between reserve and closing prices


Observations
Observations environment, and capable

  • Competition among sellers affects choice of mechanism and auction parameters

    • Important to take competition into account when designing mechanisms and bidder strategies

  • Sellers can decide to shill bid in order to improve profits

  • Mediator (such as eBay) can deter shill bidding and increase efficiency by setting appropriate auction fees


International competition
International Competition environment, and capable

  • Made proposal to have new game in the Trading Agents Competition Foundation

    • TAC Market Design

      • “Reverse” Trading Agents Competition

    • Design mechanisms with varying:

      • Clearing policy

      • Information revelation policy

      • Auction fees


Research highlights2
Research Highlights environment, and capable

  • Competing sellers in online auctions

  • Strategies for bidding in multiple auctions

  • Empirical game theory to select mechanisms and strategies for complex markets

  • Adaptive auctions


Bidding in multiple auctions

simultaneous environment, and capable

sequential

hybrid

Bidding in Multiple Auctions

  • Different start/finish times

    • Simultaneous, sequential, or hybrid

  • Heterogeneous:

    • N single-unit auctions

    • 1st/2nd price sealed bid, English or Dutch

    • Each can have different number of bidders

  • Multiple items

    Find optimal best response


Heuristic strategies
Heuristic Strategies environment, and capable

  • Setting too complex to analyse theoretically and find optimal strategies

  • Heuristic strategies:

    • Choose the thresholds

      • Single auction dominant strategy (DOM)

      • Equal threshold (EQT)

    • Choose the auction

      • Exhaustive search (ES)

      • Knapsack utility approximation search (KS)

  • Trade-off between speed and complexity


Market based control of complex computational systems


Research highlights3
Research Highlights environment, and capable

  • Competing sellers in online auctions

  • Strategies for bidding in multiple auctions

  • Empirical game theory to select mechanisms and strategies for complex markets

  • Adaptive auctions


Empirical game theory
Empirical Game Theory environment, and capable

  • Game Theory is a mathematical theory which underpins auction- and mechanism-design

    • very powerful and, at least in theory, can tell us what are the optimal mechanism and strategies.

  • But some markets too complex to analyse in practice using game theory.

    • too many participants and too many possible moves.

  • Evolutionary methods do not always converge on robust strategies

  • Empirical Game Theory:

    • emerging field combines principled game-theoretic analysis together with computer simulation methods.

    • amenable to automation, so it may be used by agents themselves to decide on market mechanisms.


Empirical game theory1
Empirical Game Theory environment, and capable

  • Analysing strategies in Double Auctions

  • Find payoffs for strategies by repeated simulations

  • Find mixture of these “pure” strategies that constitute a evolutionarygame-theoretic equilibrium


Research highlights4
Research Highlights environment, and capable

  • Competing sellers in online auctions

  • Strategies for bidding in multiple auctions

  • Empirical game theory to select mechanisms and strategies for complex markets

  • Adaptive auctions


Discrete bid english auctions

Fixed bid increment environment, and capable

Discrete Bid English Auctions


Research questions
Research Questions environment, and capable

  • What effect do these discrete bid levels have on the auction properties?

  • How should the auctioneer determine the discrete bid levels to use in any situation in order to maximise his revenue?


Calculating auction revenue

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Discrete bid levels implemented

Bidders’ valuation

distribution

Mean number of

bidders

Calculating Auction Revenue

(David et al., 2005)

  • We calculate the auction revenue by considering the probability of these three cases:

  • Gives the final result:

  • We can optimise this expression (analytically or numerically) to find the optimal discrete bid levels .


Optimal bid levels
Optimal Bid Levels environment, and capable

  • Uniform bidders’ valuation distribution

Bid increment

decreases

Reserve price

increases


Optimal bid levels1

Optimal environment, and capable

discrete bid

levels

Fixed bid

increment

Optimal

discrete bid

levels

Fixed bid

increment

Fixed bid

increment

Optimal

discrete bid

levels

Optimal Bid Levels

  • Increases expected revenue.

  • Decreases expected auction duration.

  • Increases expected auction efficiency.


Learning auction parameters
Learning Auction Parameters environment, and capable

  • To calculate optimal discrete bid levels we must know:

    • The bidders’ valuation distribution.

    • The number of participating bidders.

  • Typically we do not know these parameters.

    • However, we can use Bayesian Machine Learning to estimate these parameters – online.


Learning auction parameters1

Auction Closing Price environment, and capable

Parameter Estimates

Optimal Bid Levels

Auction

Learning Auction Parameters

(Rogers et al., 2005)

Prior

Knowledge


Bayesian machine learning
Bayesian Machine Learning environment, and capable

  • Bayesian machine learning is attractive for this application:

    • Makes use of our knowledge of how the auction closes.

    • Allows us to incorporate prior knowledge or experience.

    • Makes efficient use of the sparse training data (observations of auctions).

    • Computationally efficient (no need to maximise multi-dimensional functions).


Learning the number of bidders
Learning the Number of Bidders environment, and capable


Learning the number of bidders1
Learning the Number of Bidders environment, and capable


Conclusions
Conclusions environment, and capable

  • MBC prima facie candidate for controlling complex, distributed computational systems with autonomous self-interested components:

    • Computational game theory / Mechanism design

    • Evolutionary algorithms / Machine learning

    • Decision theory

  • Ongoing research and goals:

    • design of mechanisms and strategies for MBC

    • gain understanding of and predict dynamic properties of market-based computational systems

    • develop formal representation and tools

  • Ultimate goal: automated mechanism design


Partners
Partners environment, and capable

http://www.iam.ecs.soton.ac.uk/projects/mbc.html