This paper considers the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. It examines some complications in allocating resources between the two, particularly those introduced by the distribution of costs and benefits across time and space, and the effects of ecological interaction. Two general situations involving the development and use of knowledge in organizations are modeled. The first is the case of mutual learning between members of an organization and an organizational code. The second is the case of learning and competitive advantage in competition for primacy. The paper develops an argument that adaptive processes, by refining exploitation more rapidly than exploration, are likely to become effective in the short run but self-destructive in the long run. The possibility that certain common organizational practices ameliorate that tendency is assessed.

http://strategy.sjsu.edu/www.stable/pdf/March,%20J.%20G.%20%281991%29.%20Organization%20Science%202%281%29%2071-87.pdf

Exploration and Exploitation in Organizational Learning

Fast and frugal heuristics - simple rules for making decisions with realistic mental resources - are presented here. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? What heuristics are in the mind's adaptive toolbox, and what building blocks compose them? Can judgments based simply on a single reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? This book explores these questions by developing computational models of heuristics and testing them through experiments and analysis. It shows how fast and frugal heuristics can yield adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop-out rates, and playing the stock market.

Simple Heuristics That Make Us Smart

Fabio Arcangeli, Paul David, Frank Engelman, Christopher Freeman, Massimo Moggi, Richard Nelson, Luigi Orsenigo, Nathan Rosenberg, Michele Salvati, G. N. von Tunzelman, two anonymous referees, and the participants at the meeting of the Committee on Distribution, Growth, and Technical Progress of the Italian National Research Council (CNR), Rome, November 16, 1985, have helped with various redraftings. A particularly grateful acknowledgment is for the insightful and patient help of Moses Abramovitz. This work has been undertaken at the Science Policy Research Unit (SPRU), University of Sussex, as part of the research program of the Designated Research Centre, sponsored by the Economic and Social Research Council (ESRC). Earlier support to the research that led to this paper by the Italian National Research Council (CNR) is also gratefully acknowledged. The statistical research has been undertaken with the assistance of Stephano Brioschi, Ilaria Fornari, and Giovannu Prennushi.

https://www.jstor.org/stable/info/2726526

Sources, Procedures, and Microeconomic Effects of Innovation

This paper is a revised version of Staff Working Paper 444 It reviews various studies which have provided a description of and possible explanation to patterns of innovation adoption in the agricultural sector It therefore covers both empirical and theoretical studies The discussion highlights the diversity in observed patterns among various farmers' classes as well as differences in results from different studies in different socio-economic environments, and reviews the attempts to rationalize such findings Special attention is given to the methodologies which are commonly used in studies of innovation adoption, and suggestions for improvements of such work through the use of appropriate economometric methods are provided The diversity of experiences with different innovations in different geographical and socio-cultural environments suggest that studies of adoption patterns should provide detailed information on attributes of the institutional, social and cultural setting and their interactions with economic factors These may be an important element in explaining conflicting experiences

Adoption of agricultural innovation in developing countries : a survey (revised)

Today, as in the past, within a country at a given time those with higher incomes are, on average, happier. However, raising the incomes of all does not increase the happiness of all. This is because the material norms on which judgments of well-being are based increase in the same proportion as the actual income of the society. These conclusions are suggested by data on reported happiness, material norms, and income collected in surveys in a number of countries over the past half century.

/pdf/will-raising-the-incomes-of-all-increase-the-happiness-of-4uvil5jobh.pdf

Will raising the incomes of all increase the happiness of all

A deterministic excess demand model of stock market behavior is presented that generates stochastically fluctuating prices and randomly switching bear and bull markets.

Bulls, bears and market sheep

This paper uses the familiar, neoclassical theory of capital accumulation to show how complex behavior can emerge from quite simple economic structures. Indeed, when sufficient nonlinearities and a production lag are present, the interaction alone of the propensity to save and the productivity of capital can lead to growth cycles that exhibit a wandering, sawtooth pattern not unlike those observed in reality. These fluctuations need not converge to a cycle of any regular periodicity so they are not quasi periodic. Because such "chaotic" trajectories are unstable, errors of estimation in parameters or initial conditions, however tiny, will accumulate rapidly into substantial errors of forecast. Moreover, periods of erratic cycling can be interspersed with periods of more or less stable growth. Evidently, the "future" behavior of a model solution cannot be anticipated from its patterns in the "past," a situation that seems to mimic experience. Apparent structural change and unpredictability is explained in the present theory by a deterministic, single equation model. Random shocks play no role. So the reader can visualize just what it is we are are talking about, a noteworthy simulation is presented in Figure 1 for GNP in a growth model that is described below in Section III. A period of relatively rapid growth is followed by a period of cycles. Then, remarkably, for a considerable time (about twenty "periods") apparently "steady-state" growth occurs. Wandering cycles, however, emerge. Another brief period close to the steady state appears again toward the end of the series. I establish conditions of savings and productivity that lead to results of this kind. This analysis makes use of the mathematical theory of "chaos" which, in the form exploited here, originated in the work of Edward Lorenz. A formal definition of chaos and sufficient conditions for chaotic trajectories were provided in a seminal paper by T-Y Li and James Yorke. A survey of these related contributions is found in Yorke and Evelyn Yorke. This theory was introduced into economics by Jess Benhabib and myself (1981), where we showed that sequences of rational choices can be erratic when preferences depend on experience in a certain way; by Michael Stutzer, who provides a detailed analysis of Trygve Haavelmo's growth model; by Benhabib and myself (1980) in an application of the overlapping generations model; and in my forthcoming study of the classical growth model.

Irregular Growth Cycles

A characterization of erratic dynamics in, the overlapping generations model

In this paper we show that rational choice in a stationary environment can lead to erratic behaviour when preferences depend on experience. We mean by erratic behaviour choice sequences that do not converge to a long-run stationary value or to any periodic pattern. Early investigations of the effects of experience on choice goes back to Pareto (see also Benhabib (1979)); some Keynesian theories of the consumption function incorporate the effects of experience and habit formation on current levels of consumption (Duesenberry (1949), Modigliani (1949) and Brown (1952)); a general model of experience dependent choice was described in Day (1970) and investigated in Day and Kennedy (1971) while the specific issue of the existence of stable representable long-run consumer demand when tastes vary endogenously has been treated by Gorman (1967), Pollak (1970, 1976), Weizsaicker (1971), McCarthy (1974) and Hammond (1976). Pollak gave conditions for the representability of long-run choice when the equations of preference dependence are linear and utility additive. Hammond, in a generalization of these findings, provides conditions for stable, representable long run choice when preferences are acyclic, a condition which in essence assures convergence to a long-run choice.

Rational Choice and Erratic Behaviour

After first establishing an “a priori” expectation of nonnormality, the paper presents statistical analyses of several experimental series of field crop yields. This clinical examination suggests the following plausible inferences: The several series may be regarded as more or less random samples from stable parent probability distributions. Both normality and lognormality appear to be exceptions rather than the rule with respect to those distributions that may properly be inferred from the stochastic properties of the several series. The Pearson system of probability density functions is then applied to the data. The sample moments imply the type I (generalized Beta) skewed function of limited range as the general case. Estimates of this function are obtained for each series. The degree of skewness and kurtosis is shown to depend on nitrogen level. Implications for farm planning are discussed. Each statistical technique used is described and critically reviewed.

Richard H. Day

Papers

Bulls, bears and market sheep

Irregular Growth Cycles

A characterization of erratic dynamics in, the overlapping generations model

Rational Choice and Erratic Behaviour

Probability Distributions of Field Crop Yields