modelling of marketing and business systems: agent -based

Modelling of Marketing and Business Systems: Agent-Based Simulation

Louise YoungSchool of Marketing, University of Western Sydney

Ian Wilkinson & Fabian HeldDiscipline of Marketing , The University of Sydney Business School

Bob Marks

University of Melbourne

Terry Bossomaier

Charles Sturt University

What academics want

• New focal phenomena –dynamics not (comparative) statics

• New contexts–ROW not USA (but continuing use of TLA)

• New theories–Mechanisms and process not variables

• New methodologies–Generative social science not surveys

Two ways of doing science

“Collect observational, survey or other forms of data and analyse them, possibly by estimating a model;

ORbegin from a theoretical understanding of certain

social behaviour, build a model of it, and then simulate its dynamics to gain a better understanding

of the complexity of a seemingly simple social system”

[Introduction, Nigel Gilbert Agent Based ModellingQuantitative Methods in the Social Sciences 2008]

Artificial Network Life

• ABM allow us to synthesise new forms of behaviour and organisation that have not existed in real life

• “[Synthesis] extends the empirical database upon which the theory of the discipline is built beyond the often highly accidental set of entities that nature happened to leave around for us to study” [Chris Langton].

From statics to dynamics

• From comparative static, variables-based thinking and theories

To:• Dynamic, Process, Evolutionary and

Mechanism based thinking and theories• Problematising non-change not change

–Reproducing the past is tough• Generative Social Science (Epstein 2009)

Generative explanations

“To ’explain’ an empirical regularity is to discover a set of simple mechanisms that would produce the former in any system

governed by the later” (Herbert Simon, 1968)

THE DYNAMICS AND EVOLUTION OF BUSINESS NETWORKS

What are we trying to explain?

Example of Business Networks: Coca-Cola (simplified)Source: Press releases; McKinsey analysis

Coca-Cola

Metique

Offline Purchasing consortium

Suppliers of • Sugar

Sweetener• Coloring• Aluminum

cans• Plastic bottles• Etc.

Wal-Mart

Albertson's

Private exchange for customer data

Data

Sales data

Worldwide Retail Exchange

AOL

Transora

Direct and indirect goods

Inventory and forecast information

Procter and Gamble

NewCo("healthy" snacks and beverages)

Nestlé

Product alliance

Brand venture

Brand venture

Kraft

Direct indirect goods

Inventory and forecast data

Coca-Cola bottling company/ CCE

Distribution

Incumbent

GoodsInformation

Startup

Consortium

Logistics marketplace

GEPolymerland

Source: Science 2007

World’s Export Production - Use Network

A different way of viewing

Studying what countries make-context matters! (MIT)

What is studied here

• Production requires capabilities that are difficult to acquire/slow to accumulate, are required by some industries but not others,

• Therefore present and future capabilities are constrained by network of product similarities, i.e. products requiring similar inputs

• Case study of South Korea versus Chile– Physical and human capital of each was determines

by adjacency in product space– This adjacency determines income and speed/nature

of development

Two exemplar problems

1. Emergence of Intermediaries

2. Emergence of Central Markets

Emergence of Intermediaries: How do we get from A to B?

Buyers

Suppliers

Intermediary

Suppliers

Buyers

?

Emergence of Markets:How do we get from C to D?

Specifics? Evolution, Equillibration and Balance

• Through time systems are remade and/or evolve.

• The events of the environment precipitate events within the system. – they create synergies (Holland 1998) – bring unintended consequences (Norton 2002).

• Conceptual underpinnings of evolution can be considered in terms balance and equilibration.

Balance and Process

• “By balanced state (or situation) is meant a harmonious state, one in which the entities comprising the situation and the feelings about them fit together without stress” (Heider 1958, 180).

• Equilibration is the process by which systems retain or move towards balance.

Why balance?

• It is argued that the seeking of balance is a fundamental motivator in human psychology (Appley 1990)

• Allows us – to organise and integrate our thoughts and ideas (Festinger

1957), – To organise our thoughts and sentiments in relations with

others (Heider 1958), – is the basic means by which learning and knowledge

formation occurs (Feldman 1995). • Balance is so fundamental that humans are biologically

driven to seek it (Parkins 1990).

Moving to balance

• The process of moving towards a balanced state is equilibration.

• Equilibration has been widely considered in the social sciences

• Equilibration is also considered in the study of complex adaptive systems (eg. Prigogine 1990, Kauffman 1995, see Heylighen 1997 for an overview).

Equilibration is:

“not a simple balance of forces as in mechanics ….but in the sense of self-regulation; that is, a series of active compensations in response to external disturbances and an adjustment that is both retroactive (loop systems or feedbacks) and anticipatory, consisting of a permanent system of compensations.” (Piaget 1976, p. 74)

EXISTING RESEARCH

General Theories and Models

• IMP Interaction model- AARS e.g. Hakansson et al 1982, Hakansson and Snehota 1997, Welch and Wilkinson 2002

• Stage models e.g. Ford 1980; Dwyer, Schurr & Oh 1987

• Evolution, events, process and flow theories e.g. Bairstow 2011, Easton and Lundgren 1992, Gadde & Håkansson 1992, Hakansson 1992, Hakansson and Lundgren 1997, Hakansson and Snehota 1997, Hibbert and Wilkinson 1991, 1994, Huang 2010, Wilkinson 1990, Young 2006

• Organisational change theories e.g. Aldrich 1999, Buttriss and Wilkinson 2005, Van de Ven and Engleman 2004

• Patterns of change theories e.g.Hollander (Wheel and Accordians), Gadde and Mattsson 1987, Johanson and Hakansson 1991, Hakansson and Snehota 1997

• NK Modelse.g. Easton and Wilkinson 1996, 1997, Easton et al 1997, 2007, Wiley et al 2001

Research Methods

• Case studies and descriptive historiese.g. Ford & Redwood 2005, Fu et al 1999, Hertz 1998, Kinch 1993, Narayandas and Rangan 2004, Roy and Wilkinson 2004

• Analyses of specific mechanisms and processes e.g. Andersson and Mattsson 2009, Bairstow 2010, Bairstow and Young 2011, Easton and Araujo 1994, Halinen and

Törnroos 1995, Halinen 1998, Huang and Wilkinson 2006, Huang 2010, Medlin 2002, Wilkinson and Young 2004

• Computer simulations

– Early attempts (systems dynamics, fixed structure)e.g. Forrestor 1961, Balderston and Hoggatt 1962, Bowersox et al 1972, Wilkinson 1986

– Recent resurgence (evolving structure and dynamics)e.g. Debenham and Wilkinson 2006, Easton and Wilkinson 1997, Easton et al 1999, Easton et al 2007, Følgesvold

and Prenkert 2009, Ladley et al 2007, Rand and Rust 2011, Wilkinson and Wiley 2001

MODELING DYNAMICS AND MECHANISMS

OUR Focus: Generative explanation

Defining Mechanisms - the Verbs of Explanation

Cam

pbel

l (20

05)

the processes that account for causal relationships among variables

Hed

strö

m(2

005)

a constellation of entities and activities that are organized such that they regularly bring about a particular type of outcome

Elst

er(1

989)

the nuts, bolts, cogs, and wheels that link causes with effects

Types of Mechanisms in Business Relations and Networks

Mechanisms of specializatione.g. Acting, producing, consuming, buying, selling, learning, specialising, in-sourcing, outsourcing

Business mating mechanisms e.g. Finding, being found, attracting, repelling, choosing, being chosen

Business dancing mechanismse.g. Interacting, exchanging, responding, trusting, committing, learning and adapting in relations

Mechanisms connecting relationse.g. Enabling and constraining effects of other relations, comparing, accessing, prioritising

Environmental mechanismse.g. Enabling and constraining effects of environment and starting conditions

BUSINESS RELATIONS AND NETWORKS AS COMPLEX ADAPTIVE SYSTEMS

Complex Adaptive Systems

Locally Interacting Agents

AGENT-BASED MODELLING

ABM complements and extends existing research methods not replaces them

• Systematic case histories to identify processes and mechanisms–Qualitative historical case studies

• Validating and testing ABM against cases studies and emprical results (even SEMs!)–But agents in a model can give 100%

response rates and don’t lie!!–Need mechanisms of non response and self

serving biases in our ABM too!

Systematic Case Histories

Source: Huang 2011

Source: Huang 2011

Source: Huang 2011

Time is represented in different ways (this also emerges from using Ethno2)

Event analysis: Australian IT

Process, mechanism and evolution

The Meaning of the Lines

• How do we get from situation A (at time t) to Situation B at time t+n?

• Identifying mechanisms and processes

• Not just about where but the journey (equillibration) and the “reasons” for it.

• Attribution (direct and indirect) of reasons

Methods for identifying mechanisms in cases (methods for study of discourse)

What Business Network Mechanisms have been previously modeled?

In sum – Lots!

Computer models exist for each category of mechanisms

Many simulations use search / mating mechanisms

Strategy and strategy change mostly studied through evolutionary iterated game theory

Few examples of specializing and dividing up of labour in simulations

Limited modelling of relationship evolution processes except for evolution of cooperation and trust

BUILDING AGENT-BASED MODELS OF RELATIONS AND NETWORKS

Modules for Simulation Models ofDynamic Business Relations and Networks

Acting and specializing

Dancing

Mating

Interconnecting

Environmental impacts

Step 1: Specialization- Learning and Exchange

Acting and specializing:

• Heterogenous capabilities

• Production• Resource allocation• Specialization

through learning

Dancing:• n/a

Mating: • Exchange with random

partners

Connecting:• Scarcity of resources

Environment:• Neighborhoods• Distances• Random distribution

Simulating the Effects of Specialization and Exchange

Observed Statistics of Simulation Development

• Continuously monitoring– Deficits in supply– Progress of specialization– Energy and successful agents– Average number of potential exchange partners

Step 2: Relations Change Over Time- Stickiness

Acting and specializing:

• Homogeneous

Dancing:• Stickiness

(Age reduces chanceof switching)

Mating: • Random• Preferential

attachment• Friends-of-friends

Connecting:• Implied in Friends-of-

friends mating

Environment:• Initial network

structures differ

Simulating the Effects of Developments in a Relationship

Observed Statistics of Network Structure Development

Degree Distribution• Histogram of the

current numbers of relations for all agents

• Distribution of relationship ages

• Size of giant component

• Clustering coefficient

What stops more researchers doing this type of research?

• Ignorance and resistance (1/3, 1/3, 1/3?)

• Lack of training in high level programming–We teach maths and stats but not

programming

–Programming is different

–Programming requires algorithmic thinking (i.e. mechanisms and processes)

What can we do about it?

1. Build more user friendly programming languages

2. Provide more training in programming in schools and undergrad and postgrad programs

3. Build Flight Simulators for ABM design, development and testing

BNAS: Business Network ABM SystemA Flight Simulator for Researchers, Educators, Managers and Policymakers

Activities and specialization:

• Allocation• Production • Exchange• Learning

Dancing:• Improvements in

relationship efficiency

Mating: • Various search

mechanisms• Reputation and

expectations

Connecting:• Comparison of

alternatives and optimization

Environment:• Cost functions• Heterogeneity• Spacial distance