Organizational Memory

1. The value and dangers of remembrance in changing worlds: a model of cognitive and operational memory of organizations Giovanni Dosi, LEM, Sant’Anna School of Advanced Studies, Pisa, and visiting professor at Friedrich-Schiller-Universität, Jena Luigi Marengo, LEM, Sant’Anna School of Advanced Studies Evita Paraskevopoulou, UC3M, Departamento de Economia de la Empresa Marco Valente, Faculty of Economics, University of L' Aquila

2. Organizational Memory The ability of organizations to elicit stored information and knowledge learned throughout the organizational history that can be retrieved to bear on present decisions (Walsh and Ungson, 1991)

3. Cognitive Memory “Mental artifacts” (Levitt and March, 1988) embodying shared beliefs, strategic orientations, interpretative frameworks, codes and cultures by which the organization interprets the state of the environment and its own “internal states”.

4. Procedural Memory The ensemble of organizational routines and often “quasi genetic” action patterns (Winter in Cohen et al (1996)) elicited as a response to specific environmental or internal states.

5. Cognitive and Procedural Memory • Organizational nature • Distributed • Resilient • Entail an “if…then” structure

6. Different organizational architectures influence such distributions involving: • Distinct patterns of information flow • Different knowledge distributions (and thus locations of the “if’s” and the “then’s” across the organization) • Different divisions of cognitive and organizational labour

7. Properties of memory • Inertia • Path-dependence • Ensuing competence traps (cognitive or procedural) • Possible organizational cognitive dissonance

8. Path-dependence in organizations • Organizational Memory carries over time informational and knowledge path-dependently learned by the organization

9. Fitness Landscapes as a suggestive representation of the mapping of organizational traits into the “fitness” (i.e. some measures of performance) of the organization

10. Example 1: Single peaked • No epistatic correlation among traits • No path dependency

11. Example 2: Multi-peaked landscape • Rugged Landscape • Path dependency

12. In fact, correlated traits live on hypercubes….(example from Siggelkow and Levinthal (2005))A vivid illustration of-competence traps-effects of organizational structures upon path dependent reproduction of cognition and behaviors

14. Moving further in the exploration of organizational learning and unlearning: an explicit model of organizational cognition and action • The organizational problem is to develop a vector of interdependent actions in a complex environment characterized by a (large) set of interdependent features • The environmental configurations can be partitioned in equivalence classes, where each class requires a different action profile. • A payoff or fitness function which, for every environmental profile, gives the payoff of every action vector

15. The ambiguous role of memory in changing environments …experience supporting adaptation VS competence traps at full force…

16. And an equally ambiguous role of shocks upon memory …controversial evidence on effects on management and labour turnover…

17. Explore the foregoing properties through a model capturing organizational learning and unlearning • The organizational problem consists of interpreting a complex environment characterized by a (large) set of interdependent features and developing a vector of interdependent actions • The (large) set of environmental configurations can be partitioned in equivalence classes, where each class requires a different action profile. • A payoff or fitness function is in place which, for every environmental profile, gives the payoff of every action profile

18. Three notions of complexity of the problem • Categorizability: how large are these equivalence classes? The larger, the more invariant the action. In some of the simulations only a few environmental features (“core features”) influence the relative fitness of actions, all the others are irrelevant. • Neutrality: are such classes made of similar environmental profiles? If we modify one bit of the environmental configuration, does the fittest action tend to be same or not? • Ruggedness: if we modify one bit of the environmental configuration, do the fitness value of the action profiles tend to change smoothly or abruptly?

19. More formally • Set of environmental features: E={e1, e2,…., en}, with ei={0,1}, thus 2n environmental profiles • An action profile is the choice of values for m interdependent actions: A={a1, a2,…., am}, with ai={0,1}, thus 2m action profiles • The fitness landscape: F: E  A  R attributes a real valued payoff to each of the 2n+m environment-action states.

20. Each action is chosen by means of a system of condition-action rules when some environmental condition is met. Each rule takes the form: • c1, c2,…., ck  a1, a2,…., am with ci={0,1,#} where ci sets a condition on the i-th environmental feature, which is met if ci = ei or ci = #

21. Rules and Memory • The number of rules an agent can store is the size of his memory • If a rule’s condition matches the current environmental profile, the rule is called active • Rules that remain inactive for δ periods are discarded; δ is a memory decay parameter

22. Learning takes place through rule selection and rule modification • Rule selection: Only active rules can act. Among the active rules the one with highest fitness is chosen

23. Rule Modification • At the outset an agent is endowed with a rule whose condition is made entirely of # and a random action. Then rules are generated and modified with the following mechanisms: • On the action part local search (one-bit mutations) is performed • On the condition part two algorithms determine the generation of new rules: • specification: whenever a rule ci = # acts, it is compared with other rules which, under the current environmental state, trigger a different action mapping into a higher payoff. • generalization: if no rule is active, the one which better matches the current environmental profile generates a new one that includes enough #’s in order to become relevant and active.

24. Preliminary results and Issues • Learning in changing environments • Division of Labour

25. Learning in a simple environment • A first baseline bunch of simulations evaluate the learning properties of an agent in a “simple” landscape with three core bits. • Our learning agent develops rules that correctly match environmental and action profiles. • Initially we have an exploration phase in which a large number of new rules are generated with very low degree of specificity. • At a later exploitation stage, actions become increasingly tailored to the correct environmental conditions. This process is generated by the cumulation of evidence that a given rule is systematically better when the set of core bits are in a given configuration.

26. Exploration in a simple environment

27. A second baseline bunch of simulations evaluate the learning properties of an agent in a “complex” landscape. • If the number of core bits is low (i.e. the landscape is locally rugged but with a lot of neutrality), learning is much slower that in a simple landscape, gets stuck in many local optima.

28. Complexity of the environment and number of rules

29. Learning in a complex environment • A second baseline bunch of simulations evaluate the learning properties of an agent in a “complex” landscape. • If the number of core bits is low (i.e. the landscape is locally rugged but with a lot of neutrality), learning is much slower than in a simple landscape, the organization gets stuck in many local optima.

30. If we increase the number of core bits ,the learning processes settles into a much lower number of more general rules (routines emerging)

31. Changing Environments at different speeds

32. The Marks of path-dependency • Even in unchanging environments , firm-specific cognitive frames and action repertoires…

33. Persistent cognitive and operational diversities across firms

34. When (path-dependent) memory becomes an obstacle to adaptation: environmental “punctuation”…

35. “Punctuated Equilibria” with system-wide shocks:Rule specificity

36. “Punctuated Equilibria” with system-wide shocks:Rule age

37. “Punctuated Equilibria” with system-wide shocks:Relative Fitness

38. In general, de-locking mechanisms • Purposeful loss of memory • Changes in organizational structure • Increasing “cognitive dissonance” between organizational cognitive frames and action repertoire • Management and labour turnover

39. Division of Labour and Organization Suppose now the organizational problem is decomposed into sub-problems assigned to different agents (“division”). Each division observes a subset of the environmental features and chooses a subset of actions according to a local fitness indicator

40. Issues involved and preliminary results (I) Decomposability (modularity) of the problem and coordination: if the organizational problem is perfectly decomposable and the organization reproduces this decomposition coordination is easily achieved

41. Issues involved and preliminary results (II) • Memory and local knowledge: if divisions have homogeneous cognitive and memory capacities, the smaller the decision modules the more they can develop specific knowledge • There may be however an evolutionary advantage (already shown in Dosi-Marengo, JEBO) for organizational structures with decompositions finer than optimal: they tend to get stuck in local optima but they climb them more quickly

42. Further exploration ahead • Local and global shocks upon memory (turnover) • Organizational cognitive dissonance (partial decoupling of “if…then” rules)

43. Next to explore…