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Gavetti, Levinthal, & Rivkin (2005): The power of analogy. How firms discover effective competitive positions in complex and novel situations? Three types of search Rational/deductive Surveying a map and then choosing the best position (vs. Bounded rationality) Analogical
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Gavetti, Levinthal, & Rivkin (2005): The power of analogy • How firms discover effective competitive positions in complex and novel situations? • Three types of search • Rational/deductive • Surveying a map and then choosing the best position (vs. Bounded rationality) • Analogical • Comparing the novel situation to a similar one to find the best starting point • Local search • Starting from a random point and then climbing higher • The paper uses a simulation to compare the differences between local search and analogical reasoning
Reasoning by analogy • A mental representation of the target problem (a lower dimensional sketch) • Look for other settings (source) that remind the target problem and you are familiar with • Develop a candidate solution in the source and transfer that to the the target
Observable charactiristics X=(x1, x2, ..., xN) 1. Which dimensions (xi) are the most relevant ones? 2. Which situation from my experience is the most similar to this one? Experience landscape #A Observable charactiristics X=(x1, x2, ..., xN) Experience landscape #B mgr Novel and complex landscape Experience #N Breadth Experience #N 3. Think what would be the best solution in the source and apply it to the new one Experience landscape #Z
Landscape Policy (1 or 0) Observable characteristics P1 P2 P3 x1 x2 xN D1 D2 D3 D4 D5 D6 D7 D8 D9 Each xi may or may not influence the impact of D’s Detail (1 or 0) Each landscape has its optimal position in some combination of D’s D’s have two kinds of interdependencies Kw: within a policy, e.g. D1 influences how D3 will influence Kb: between policies, e.g. P2 influences how D3 will influence Depth of a managers experience tells how well she knows the optimal values for the given landscape
Parameters • Landscapes • P: The number of highlevel policy decisions • D: The number of detailed decisions • X: The total number of observable characteristics • Kw: Interdependence bw/ detailed decisions within a policy • Kb: Interdependence bw/ other policies and focal detailed decision • X(prob) probability each xi influences the impact of D’s • Firm search • Depth: How well the manager knows the source • Breadth: How many source maps the manager has • <###> which observable characteristics the manager thinks matter • Orthodoxy: Does the manager hold on to the first policy set or not, when making detailed decisions
Findings • Analogical search better than local search • Breadth of experience more important than depth • Knowing the right x’s is important • Should not hold on to analogies too firmly • analogical search provides the starting point for search (i.e. heterodox better than orthodox, when the analogy is not perfect)
Farjoun (2008) Commentary on GLR(2005) • More constructive view on strategy • Environments are not given, but company actions shape the landscape • Other variants of rational choice, incremental strategy and AR can also contribute • GLR used simplified ideal types • Other strategy making approaches • Mental experimentation and case-based models
Farjoun (2008) Commentary on GLR(2005) • Mental experimentation & Abduction • Reasoning from the experience of a case and develop a meaningful and plausible interpretation • Visioning, scenario alaysis, system dynamics, simulations = variants of ”what if” thinking • Broadening the dialogue • Matching search processes with contingencies (Table 2) • Combining search models
Gavetti, Levinthal, Rivkin (2008) Response to Farjoun • Analogical reasoning allows constructive view on strategy • E.g. Sony’s learnings from Betamax to Blu-ray • ”The N choises ... can include choices that are intended to shape one’s environment” (p. 1019) • NKC models: A’s actions influence B’s landscape and thus actions, influencing A’s landscape • Other search modes • Important to understand each thoroughly rather than have a laundry list • Projection, associative reasoning, and feedback driven learning • Contingencies • Use formal modeling to be explicit and to understand complex relations