The Uses of Diversity. David Ellerman

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СКАЧАТЬ See Jacobs’s analysis (1992) of corruption resulting from a “monstrous hybrid” between the commercial and guardian syndromes.

      6. See Goldberg (1980) for the contrast between relational and arms-length contracts.

      7. For instance, many Panglossian treatments of migration and development see people using the “exit to find a better home” logic but then either returning from a “temporary” migration with needed skills or at least sending back remittances to fuel local development so that the virtue of “committing to make home better” will also be realized. But, as the saying goes, “there is nothing more permanent than ‘temporary’ migration” and the remittances are spent largely for consumption purposes; see Jacobs (1984, 122) on remittances; and overall, see Ellerman (2003a). In this case, the desire to take the exit logic but still get the virtues of the other logic has largely proved to be a vain hope.

      8. See, for example, Pagels (1988) or Axelrod and Cohen (1999).

      9. Economists often assume a simplified “one hill” model where hill-climbing mechanisms (local optimization) will suffice.

      10. Hence, probability theory neglects the dual risk reduction strategy when it takes the probability distribution as being independent of the “bets” made on each outcome.

      11. For instance, the advice of the World Bank to developing countries about labor is in the “labor markets” topic area; there is no “human resources” topic area. But for its own staff within the World Bank, there is a Human Resources vice president but no “Labor Market Vice President.” Thus, the Bank staff looks outward for clients through an exit-oriented lens and inward for themselves through a commitment-oriented lens.

      12. Notice that the exercise of voice now appears as the exercise or calling forth of effort broadly interpreted. Although the terminology of X-efficiency came later, Hirschman has emphasized the importance of the alternative notion of efficiency in economic development: “development depends not so much on finding optimal combinations for given resources and factors of production as on calling forth and enlisting for development purposes resources and abilities that are hidden, scattered, or badly utilized” (1958, 5).

      13. For instance, in the management literature this is closely related to W. Richard Scott’s (1998) distinction between a corporation as a “rational system” and as a “natural system.”

      14. A perfect labor market is similar to perfect insurance in that it gives workers no incentive to put forth effort (i.e., improve their effort characteristics) since, if caught shirking, they can exit and costlessly find the equivalent job elsewhere. The efficiency wage hypothesis is that firms will introduce imperfections by paying more than the supply price for a standard type of work so that workers will have something to lose if found to be shirking. See Akerlof and Yellen (1986).

      15. See Clark (1979) or Dore (1987) for similar tables.

      16. In the same vein, Hirschman refers to “that ‘long confrontation between man and a situation’ (Camus) so fruitful for the achievement of genuine progress in problem-solving” (Hirschman 1973, 240).

      17. The reference to the employment relationship as the renting of persons is unusual but technically correct and not a point of controversy; it is fully acknowledged in conventional economics itself. As Paul Samuelson (1915–2009), the first American Economics Nobel prize winner, put it in his economics textbook: “Since slavery was abolished, human earning power is forbidden by law to be capitalized. A man is not even free to sell himself: he must rent himself at a wage” (Samuelson 1976, 52 [his italics]).

Chapter 3

       Parallel Experimentation

      Introduction

      In the late nineteenth and early twentieth centuries, evolutionary analogies smacked of the cut-throat, survival-of-the-fittest, and red-in-tooth-and-claw imagery of social Darwinism and free-market economics. Today this has much changed due in part to revolutions in biology itself and in part to the modeling of “biological” processes using very different substrata in the literature on artificial life and complex adaptive systems. We are freed to use biological metaphors in many different fields. Dynamic processes first discovered in biology might be abstractly formulated and be found to have applications in quite different fields (e.g., genetic algorithms or predator-prey dynamics). It could also work in reverse. We might first isolate fundamental processes at work in other areas and then look to see if the “blind watchmaker” (Dawkins 1986) of evolution had, perhaps, found similar mechanisms long ago.

      My topic in this chapter is the process of parallel experimentation, which I take to be a process of multiple experiments running concurrently with some form of common goal, with benchmarking comparisons made between the experiments, and with the migration of discoveries between experiments wherever possible to ratchet up the performance of the group. The thesis is that this is a fundamental scheme to enhance variation, innovation, and learning in contexts of genuine uncertainty.

      The use of a parallel-path strategy for the solution of difficult development problems is standard practice in several of our outstanding industrial laboratories. It is extremely common in agricultural and medical research. And in the atomic-bomb project, one of the most spectacularly successful military projects the United States has ever undertaken, the parallel-path strategy was employed. (Nelson 1961, 353)

      This set of ideas about innovation and learning keeps popping up in different fields including evolutionary theory, so my aim here is to try to draw out the analogies and triangulate on the ideas.

      The theme of parallelism has received renewed attention in the modern complexity sciences, for example the brain-inspired theories of neural networks and parallel distributed processing.

      [The] theme under the aegis of complexity is the emphasis on parallel (network) rather than serial (hierarchical) systems. . . . The distinction between serial and parallel systems is quite general. The serial system can be generalized to a hierarchical system like the pyramidical organization chart for a corporation, the church, or the military. Hierarchical systems are such that there are a “top” and a “bottom” at every level. . . . Parallel systems generalize to what I will call a “network.” A network has no “top” or “bottom.” Rather it has a plurality of connections that increase the possible interactions between components of the network. Most real system are mixtures of hierarchies and networks. (Pagels 1988, 50)

      It will be useful, however, not to cast our net too widely. As considered here, parallel experimentation assumes enough of a common goal—a cooperative aspect—so that benchmarking between experiments is meaningful and discoveries could be usefully communicated between experiments to ratchet up the whole group.¹ Perhaps the borderline case is the common experimental design to concurrently test a number of already-delineated treatments on random samples of individuals to decide which is best. Ronald A. Fisher’s F-test provides a type of benchmarking between the treatment groups to see if the variance between groups is significantly different from the variance of individuals within groups.

      Sewall Wright’s Shifting Balance Theory of Evolution

      Sewall Wright (1889–1988) together with the same Ronald A. Fisher and J. B. S. Haldane were the three progenitors of one of the revolutions in modern biology, the mathematical theory of population genetics (see Provine 1971). In the recent complexity science literature, Wright is more often mentioned as the inventor of the “fitness landscape” to represent optimization on a very rugged and cloudy landscape. Yet СКАЧАТЬ