university of navarra complexity and strategy eiasm academic council prof. joan e. ricart iese...

University of Navarra

Complexity and Strategy

EIASM Academic Council

Prof. Joan E. RicartIESE Business School

October 11, 2006


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Prof. Joan Enric Ricart [email protected]

Agenda

1. Limits on classical organizational dynamics

2. Complex adaptive systems

3. Complexity in management literature

4. An example: Corporate level decision in turbulent environments.


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Analysis anddiagnostic

Of the current statusChoose corrective

action

Select and Realization of

the futureImplement corrective

action

ResultsForecast vs. realized

FeedbackDeviation fromplan

Classical Organizational Dynamics

The search for equilibrium

BASED ON: The search of a goal and the need to adapt to the environment: Strategic Planning and Control Systems


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1. Not possible to use scenarios for all possible events.

2. From the 70’s more difficult to forecast due to:Deregulation and privatizationGlobalizationTechnological development

3. Three key ignored factors:The existence of positive feedbacksAmbiguity and paradox are inherent to the firmThe social construction of reality

Limitations

Classical Organizational Dynamics


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Four Alternative Views

Process Engineering

SystemsDynamics

Mathematical Complexity

Social Complexity

Taylor, Demming, Hammer, Argyris, Senge, Checkland,

Langton, Kauffman, Wolfram Stacey, Cilliers, Juarerro

ObjectiveRule-based

Subjective Heuristics-based

Established OrderProspective Coherence

Emergent Order Retrospective

Coherence

Complexity sciences as an explanation of how novelty emerges


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Complex Adaptive Systems: Definition

• Nº of agents behaving according to their own principles of local interaction (“Microinteractions”)

Stable equilibrium Random chaos Edge of chaos

• Patterns of evolution emerge in the interaction between agents, neither by choice of “designer” nor by chance

No agent capable of determining patterns of whole system No agent is “designer” from outside the system CAS also display a broad category of dynamics

Eg: food distribution in a big city


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Complex Adaptive Systems: Modeling in biology

K=0“smooth landscape”

stable attractorsurvival strategy is easy to copyremove “competitive advantage”

K is high“rugged landscape”

high number of attractorsextreme: properties of mathematical chaos

as conflicting constraints multiply

Fitness reflected by height of positions in a “fitness landscape”

• Network evolve trying to survive increasing their fitness

Highest fitness at intermediate levels of K “edge of chaos”

N entities or agents form the network (gene)K= nº of connectionsThe different agents can take two values (0;1)Each value has an associated “fitness value

• Kauffman’s NK boolean networks (biology, genetics)


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Complexity in management literature

1. Complexity sciences used as source of loose metaphors

2. Complexity sciences as a framework about learning systems

• NK modeling

• Industry-level studies

• Self-organized interaction driven by simple rules “hidden order”

• Dynamics at “the edge of chaos”

• Fitness landscapes as set of possible structures to choose


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“Self organizing based in Simple rules”

“Designed emergence” (Pascale, 1999) They choose broadly what emerges

• Idea: Managers should manage the context and allow self- organizing to arise fruitfully (Morgan, 1997; Eisenhardt & Brown, 1998) Issue set of “simple rules” (Eisenhardt & Sull, 2001) Let the organization evolve freely within them

• Managers condition emergence

• Implications The message of complex sciences on how novelty emerges is lost Designer of “simple rules” outside the system No novelty, just unfolding of states within the simple rules Message already present in Systems Dynamics Emergence “allowed” only at superficial level Control is centralized in “designer", not property of micro-interactions


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NK networks in Social Science

• Problem: biology assumes total decomposability of the network

• Several papers use NK networks in social science

• Solution: works assuming near decomposability (Gavetti, 1999; Gavetti, Levinthal & Rivkin, 2003; Caldart & Ricart 2003, Siggelkow & Levinthal, 2003; Siggelkow and Rivkin, 2003)

Levinthal (1997), Mc Kelvey (1999)

High level decisions impose “majority rule” to low level decisions

High level decisions made on the basis of bounded knowledge of the network’s payout (fitness) structure

Decomposability solved by bringing back “the designer” to the picture

Firms are near decomposable systems (Simon, 1968) Interactions within units more intense than between units


An example

”Corporate Level Decisions in Turbulent Environments:

A View from Complexity Theory”

Adrián Caldart & Joan Enric Ricart


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Long lasting (and open) debate on whether and how thecorporate level contributes to competitive advantage

CL contributes (Brush & Bromiley, 1997; Bowman & Helfat, 2001) CL doesn’t contribute. (Schmalensee, 1985; Rumelt, 1991; Mc Gahan & Porter 1997)

Mixed results suggest that new approaches would be welcomed

Recent literature focuses on design issues approached from the complexity paradigmCase studies of companies exposed to “turbulent environments”

Turbulent environments: high dynamism, complexity and uncertainty

(Galunic & Eisenhardt, 2001; Chakravarthy et al, 2001)Agent based simulations exploring how design issues affect firm’s evolution

(Levinthal, 1997; Mc Kelvey 1999; Gavetti and Levinthal, 2000)

Research Question:

How does the corporate level affect competitive advantage in turbulent environments?

Motivation


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Framework :Corporate Strategy Triangle

Purpose: to provide lenses to approach the field study

Cognition Representing the fitness landscapeImperfect due to bounded rationality

Corporate search strategyLocal searchOn line long jumps (commitment)Off line long jumps (real options,

alliances)Recombination

Architectural design Management of interdependenciesCenter-unit / Unit-Unit. Self-organizationAction-payoff relationshipsBalance. Prevent “error” or “complexity” catastrophes

CorporateStrategy


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Simulation experiments: Purpose • To explore the relationship between the three building

blocks of the CST in a formal and general way

• To observe the behavior and the relative performance of varied configurations of the CST under different environmental settings

• Findings in a previous fieldwork (Caldart & Ricart; 2003) led us to explore a particular concern:

Environmental turbulence requires to increase internal

complexity (Ashby’s law). Then,

Should a change in internal complexity affect qualitatively

corporate strategy making?


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Simulation experiments: Model Adaptation of Kauffman’s NK model

Simulated firms have P=3 divisions with D=3 functionalpolicies each (N=9). Hierarchy of choices.

Parameter K is divided in two: KW (intra-divisional links) and KB (interdivisional links)

Divisional strategy limited by majority ruleEight possible corporate strategies (23)Each CS has 64 possible configurations (43)

Firms are assumed to match environmental variety throughtheir architectural design (Ashby’s law)

Higher KW and KB imply an attempt to match internally a higherdegree of external turbulence

Software: Java-based ad-hoc program


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Simulation experiments: Model Seven Evolution Patterns (combinations of cognition and search strategy)

are released on each kind of landscape:

Corporate Strategy Cognition1 Intelligent Disciplined (ID) Smart Local search led by cognition

2 Intelligent Moderately Flexible (IMF) Smart Local search led by cognition if successful in long term

Otherwise, purely experiential search

3 Intelligent Highly Flexible (IHF) Smart Local search led by cognition if successful in short-term


4 Mediocre Disciplined (MD) Poor Local search led by cognition

5 Mediocre Moderately Flexible (MMF) Poor Local search led by cognition if successful in long term


6 Mediocre Highly Flexible (MHF) Poor Local search led by cognition if successful in short-term


7 Totally Emergent and Flexible (EF) None Purely experiential search

Search Strategy


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Simulation experiments: Model

Each evolution pattern is released on eight kinds of fitness landscapes, each of them reflecting different structural designs

A Kb=0 implies an M-form design As Kb increases, we have increasingly complex CM-form designs 7 different patterns of evolution under eight different architectural

designs conform 56 configurations of the CST

Architectural designKb: interdivisional interdependencies

Kw: intradivisional interdependencies

KB

0 0,25 0,5 0,750

KW 1 Stable Relat. Stable Relat. Turb. Turbulent2 Stable Relat. Stable Relat. Turb. Turbulent

Cases matching Simon's near decomposability assumption


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Simulation experiments: Simulation run II Relatively Turbulent Environment

KW=1 KB=0.5

0,45

0,5

0,55

0,6

0,65

0,7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Emergent Flexible Intelligent Disciplined Intelligent Highly Flexible Intelligent Moderately Flexible

Mediocre Disciplined Mediocre Highly Flexible Mediocre Moderately Flexible


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Simulation experiments: Findings

KW=1; KB=0 KW=2; KB=0 KW=1; KB=0,25 KW=2; KB=0,25 KW=1; KB=0,5 KW=2; KB=0,5 KW=1; KB=0,75 KW=2; KB=0,75

ID ID ID ID ID ID ID IDIMF IMF IMF IMF IMF IMF IMF IMFMD MD IHF IHF IHF IHF IHF IHFIHF IHF MD MD MHF MHF MHF MHF

MMF MMF MMF MMF MMF MMF EF EFMHF MHF MHF MHF EF EF MMF MMFEF EF EF EF MD MD MD MD

Intelligent cognitionMediocre cognitionTrial and error

Stable Environment

Relatively StableEnvironment

Relatively Turbulent Environment

TurbulentEnvironment


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Simulation experiments: Findings The importance of cognition is contingent to the degree of

environmental turbulence

• Stable environments: discipline ALWAYS pays • Turbulent environments: discipline only advisable if cognition is

“intelligent”.

In turbulent environments, if the initial cognition is mediocre, results favor strategies based on its opportunistic application

• Realized strategy as a mix of intended and emergent features Purely opportunistic strategy always underperforms


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General discussion and conclusions

Corporate Strategy

Decision level that drives, paces and frames corporate wide evolution through the choice, at the corporate level of the firm, of a particular equilibrium configuration of the CST.

Evolution is driven by the cognitive representation

Corporate decisions pace evolution shifting between initiatives that involve local search/long jumps/recombinations

The corporate level develops broad organizational arrangements that frame the emergence of self-organized processes as sources of corporate advantage

university of navarra complexity and strategy eiasm academic council prof. joan e. ricart iese...

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