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S-D Logic Thinking Small and Long: Service-Dominant Logic & Agent Based Modeling Robert F. Lusch Lisle & Roslyn Payne Professor of Marketing University.

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Presentation on theme: "S-D Logic Thinking Small and Long: Service-Dominant Logic & Agent Based Modeling Robert F. Lusch Lisle & Roslyn Payne Professor of Marketing University."— Presentation transcript:

1 S-D Logic Thinking Small and Long: Service-Dominant Logic & Agent Based Modeling Robert F. Lusch Lisle & Roslyn Payne Professor of Marketing University of Arizona University of Hawaii March 10, 2006

2 S-D Logic Small and Long Thinking S-D LogicAgent Based Modeling Thinking Small All agents exchange service or competences. Agent microscopic actions and interactions. Thinking Long All economies are service economies. Evolution of complex adaptive systems.

3 S-D Logic S-D Logic & ABM as a Paradigm Shift: From Constructs to Actors Virtually all social science theory models relations between constructs. S-D logic views marketing as interactions between entities and ABM provides the method to model and research these interactions. What emerges from interactions? Macro structures Relations between variables Rules (institutions and norms) Co-creation

4 S-D Logic Building Markets from Ground Up Digital OrganismsGenetic algorithms Fuzzy LogicData Capturing & Aggregation Object Oriented Programming

5 S-D Logic Object Oriented Programming OOP Integrates Data and Functions. Every digital organism is an object with its own information and functions it uses to operate. Every digital organism has receptors, memory, decision system, and effectors.

6 S-D Logic Creation of Digital Life Environment Sensory Capability Memory Capability Learning & Decision Capability Effector Capability Object Oriented Software Program

7 S-D Logic Genetic Algorithms & Digital Learning Learning ModeGenetic Mechanism ImitationSelection & Reproduction CommunicationCrossover ExperimentationMutation

8 S-D Logic Decision-Making: From Substantive Rationality to Procedural Rationality Simon (1978) argues the concept of rationality is economics main export to other social sciences. In complex environments actors evolve and their actions and anticipations are unknown from each other; the relevant rationality is procedural rationality. These environments are the permanent and ineradicable scandal of economic theory (Simon 1976). Mind is the scarce resource; how the actor finds efficient and effective search algorithms is the key.

9 S-D Logic Procedural Rationality: How do Individuals Reason & Learn? Inductive reasoningampliative method of reasoning (gap filling) Extinguish rules or actions that are unsuccessful and adopt rules or actions that are successfulmarket hypotheses Information processing and actions not fine-grained but are fuzzy Memory lingers; little is completely forgotten

10 S-D Logic Fuzzy Logic Lack of crisp, well- defined boundaries Membership in two or more sets Imprecise linguistic concepts Everything a matter of degree Speed of perception and information processing Weekend Days Saturday Sunday Friday

11 S-D Logic A Pair of Interesting Observations What used to work no longer works? Competitive dynamics Competition is a disequilibrating process If it works dont fool with it. Learning via exploitation Learning via exploration The ambidextrous organization

12 S-D Logic Real Competitive Markets Competition is an evolutionary & disequilibrating process (Schumpeter 1934; Alchian 1950; Nelson & Winter 1982) Competition occurs in uncertain world and competition is a knowledge discovery process (Hayek 1935) Demand and supply are heterogeneous (Chamberlain 1933; Alderson 1957, 1965) Competition involves a struggle for advantage (Clark 1954; Alderson 1957, 1965) History counts (North 1981; Chander 1990) Entities constantly strive to do better (Bain 1954, 1956) Resources are tangible and intangible and imperfectly mobile (Penrose 1959; Lippman & Rumelt 1982). Knowledge is the fundamental source of competitive advantage (Vargo & Lusch 2004).

13 S-D Logic Competitive Dynamics: Simple Rules Sellers must independently decide on price, advertising, product attributes, inventory level. Seller has four fuzzy states (low, moderately low, moderately high, high) for each of four decisions. 4 4 = 256 rules These 256 rules form a market hypothesis Ten rule bases characterize 10 market hypotheses each seller uses. Utilization of which market hypothesis to use is based on their fitness.

14 S-D Logic Simple Setting: Complex Market Buyers are heterogeneous with preferences in n- dimensional product space. Sellers have cost functions and decision alternatives. Decisions include price, advertising, product attributes, inventory. Buyer demand is a function of price, advertising, product offering, social capital. Buyer demand function is homogeneous and non-changing. Sellers have four fuzzy states for each of four decisions. Thus each seller has 256 rules which for a market hypothesis. Each seller has 10 market hypotheses. Each market hypothesis has a fitness function

15 S-D Logic How Fuzzy Inputs Interact to Affect Price Decision

16 S-D Logic Evolution of Profit Payoff from Price: Seller-1

17 S-D Logic Evolution of Profit Impact from Price Across Sellers

18 S-D Logic Evolution of Cross Profit Impact from Price: Sellers 1 &2

19 S-D Logic The Ambidextrous Organization & Evolutionary Biology When the environment changes slowly then mechanisms of exploitation that work on variation, selection and retention work well.We learn by communicating and do this primarily by crossover. When there is dramatic shift in the environment or a punctuated equilibria then relying purely on exploitation will not allow the organism to survive. It must explore to innovate or face extinction.

20 S-D Logic The Ambidextrous Organization: Modeling Exploitation with Crossover Moderate Crossover (moderate exploitation) is represented by 50% probability of crossover every 30 periods. High Crossover (high exploitation) is represented by 100% probability of crossover every 30 periods. In this situation the seller takes advantage of every opportunity to investigate the space for a good solution.

21 S-D Logic The Ambidextrous Organization: Modeling Exploration with Mutation High Mutation (high exploration) is represented by 50% probability of mutation every 30 periods. Moderate Mutation (moderate exploration) is represented by 25% probability of mutation every 30 periods. Low Mutation (low exploration) is represented by 5% probability of mutation every 30 periods.

22 S-D Logic Simple Setting: Complex Market Buyers are homogeneous. Buyers in market-A are stable and do not change their preferences but in market-B change their preferences randomly every 1500 periods. Sellers have cost functions and decision alternatives. Decisions include price, product attribute, production level. Buyer preference is a function of price and product offering. Sellers have four fuzzy states for each of three decisions. Each market hypothesis has 64 rules. Sellers vary in the exploration & exploitation.

23 S-D Logic Organizational Learning Strategies Low Exploration Moderate Exploration High Exploration Moderate Exploitation Seller-Two Crossover =.5 Mutation =.25 Seller-One Crossover =.5 Mutation =.5 High Exploitation Seller-Four Crossover = 1.0 Mutation =.05 Seller-Three Crossover = 1.0 Mutation =.25

24 S-D Logic Market-A: Stable World Buyer preferences are fixed or unchanging. In this situation we would expect the organization that focuses heavily on exploitation as a learning mechanism and seldom uses exploration to learn to perform best (seller four). On the other hand an organization with high exploration would do poorly (seller one).

25 S-D Logic

26 S-D Logic

27 S-D Logic Stable World

28 S-D Logic Market B: Turbulent World Buyer preferences are randomly changed every 1500 periods (50*crossover frequency). In this situation we would expect ambidextrous organizations to do best. The organizations that both, to a good degree, exploit and explore. This would be sellers 2 or 3. Seller four who hardly ever explores should perform the poorest.

29 S-D Logic Turbulent World

30 S-D Logic Profit Payoffs Stable Environment Turbulent Environment Seller-1 (low exploit; high explore) ($256,372)$185,182 Seller-2 (low exploit; mod explore) ($247,593)$105,849 Seller-3 (high exploit; mod explore) ($ 52,813)$307,339 Seller-4 (high exploit; low explore $417,781($46,703) TOTAL MARKET($138,997)$551,667

31 S-D Logic Moderating Effect: Market Environment (average profit)

32 S-D Logic Concluding Observations


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