Evolution as computation: integrating self-organization with generalized Darwinism

Abstract:Generalized Darwinism and self-organization have been positioned as competing frameworks for explaining processes of economic and institutional change. Proponents of each view question the ontological validity and explanatory power of the other. This paper argues that information theory, rooted in modern thermodynamics, offers the potential to integrate these two perspectives in a common and rigorous framework. Both evolution and self-organization can be generalized as computational processes that can be applied to human social phenomena. Under this view, evolution is a process of algorithmic search through a combinatorial design space, while self-organization is the result of non-zero sum gains from information aggregation. Evolution depends on the existence of self-organizing forces, and evolution acts on designs for self-organizing structures. The framework yields insights on the role of agency and the emergence of novelty. The paper concludes that information theory may provide a fundamental ontological basis for economic and institutional evolution.</jats:p

Toward a New Ontological Framework for the Economic Good

In this invited comment piece, I argue that the Lima de Miranda and Snower SAGE framework represents not just another “beyond GDP” alternative but is an important contribution to a larger shift underway in economics regarding our understanding of human behavior and the nature and purpose of economic systems. Recognizing this broader shift helps us see how SAGE might be strengthened and built upon. In this spirit, I suggest some starting points for strengthening the normative foundations of the SAGE framework, discuss an alternative interpretation of the welfare effects of inequality, propose further work on the “material gain” part of the framework, and and briefly suggest an alternative approach to SAGE’s utility maximizing decision model. I conclude that SAGE provides a framework for a very rich future research agenda.</jats:p

Biophilic Markets

Abstract Markets must be made biophilic: that is, compatible with life flourishing on Earth. To do so, we must abandon prevailing notions of market efficiency and reconceive markets as social evolutionary systems embedded in nature. Such a reconception enables us to see that constraining markets within biophysical boundaries would not result in zero-sum trade-offs with the economy, but instead would drive market evolution to new forms of prosperity.</jats:p

Getting big too fast: Strategic dynamics with increasing returns and bounded rationality.

Abstract Neoclassical models of strategic behavior have yielded many insights into competitive behavior, despite the fact that they often rely on a number of assumptions-including instantaneous market clearing and perfect foresight-that a broad range of research show to be incorrect. Researchers generally argue that these assumptions are &quot;good enough&quot; to predict an industry&apos;s probable equilibria, and that disequilibrium adjustments and bounded rationality have limited competitive implications. Here we focus on the case of strategy in the presence of increasing returns to highlight how relaxing these two assumptions can lead to outcomes quite different from those predicted by standard neoclassical models. Prior research suggests that in the presence of increasing returns, tight appropriability and accommodating rivals, in some circumstances early entrants can achieve sustained competitive advantage by pursuing Get Big Fast (GBF) strategies: rapidly expanding capacity and cutting prices to gain market share advantage and exploit positive feedbacks faster than their rivals. Using a simulation of the duopoly case we show that when the industry moves slowly compared to capacity adjustment delays, boundedly rational firms find their way to the equilibria predicted by conventional models. However, when market dynamics are rapid relative to capacity adjustment, forecasting errors lead to excess capacity, overwhelming the advantage conferred by increasing returns. Our results highlight the risks of ignoring the role of disequilibrium dynamics and bounded rationality in shaping competitive outcomes, and demonstrate how both can be incorporated into strategic analysis to form a dynamic, behavioral game theory amenable to rigorous analysis

Financial support provided by the Project on Innovation in Markets and Organization at the MIT Sloan School of Management. We thank Bob Gibbons and Nelson Repenning for helpful suggestions. Getting Big Too Fast: Strategic Dynamics with Increasing Returns

Abstract Prior research on competitive strategy in the presence of increasing returns suggests that early entrants can achieve sustained competitive advantage by pursuing Get Big Fast (GBF) strategies: rapidly expanding capacity and cutting prices to gain market share advantage and exploit positive feedbacks faster than their rivals. Yet a growing literature in dynamics and behavioral economics, and the experience of firms during the 2000 crash, raise questions about the GBF prescription. Prior studies generally presume rational actors, perfect foresight and equilibrium. Here we consider the robustness of the GBF strategy in a dynamic model with boundedly rational agents. Agents are endowed with high local rationality but imperfect understanding of the feedback structure of the market; they use intendedly rational heuristics to forecast demand, acquire capacity, and set prices. These heuristics are grounded in empirical study and experimental test. Using a simulation of the duopoly case we show GBF strategies become suboptimal when market dynamics are rapid relative to capacity adjustment. Under a range of plausible assumptions, forecasting errors lead to excess capacity, overwhelming the cost advantage conferred by increasing returns. We explore the sensitivity of the results to assumptions about agent rationality and the feedback complexity of the market. The results highlight the risks of incorporating traditional neoclassical simplifications in strategic prescriptions and demonstrate how disequilibrium behavior and bounded rationality can be incorporated into strategic analysis to form a dynamic, behavioral game theory amenable to rigorous analysis

Getting Big Too Fast: Strategic Dynamics with Increasing Returns and Bounded Rationality

Neoclassical models of strategic behavior have yielded many insights into competitive behavior, despite the fact that they often rely on a number of assumptions--including instantaneous market clearing and perfect foresight--that have been called into question by a broad range of research. Researchers generally argue that these assumptions are "good enough" to predict an industry's probable equilibria, and that disequilibrium adjustments and bounded rationality have limited competitive implications. Here we focus on the case of strategy in the presence of increasing returns to highlight how relaxing these two assumptions can lead to outcomes quite different from those predicted by standard neoclassical models. Prior research suggests that in the presence of increasing returns, tight appropriability, and accommodating rivals, in some circumstances early entrants can achieve sustained competitive advantage by pursuing "get big fast" (GBF) strategies: Rapidly expanding capacity and cutting prices to gain market share advantage and exploit positive feedbacks faster than their rivals. Using a simulation of the duopoly case we show that when the industry moves slowly compared to capacity adjustment delays, boundedly rational firms find their way to the equilibria predicted by conventional models. However, when market dynamics are rapid relative to capacity adjustment, forecasting errors lead to excess capacity--overwhelming the advantage conferred by increasing returns. Our results highlight the risks of ignoring the role of disequilibrium dynamics and bounded rationality in shaping competitive outcomes, and demonstrate how both can be incorporated into strategic analysis to form a dynamic, behavioral game theory amenable to rigorous analysis.marketing, applications, simulation, strategy, bounded rationality

Forthcoming Management Science Financial support provided by the Project on Innovation in Markets and Organization at the MIT Sloan School of Management. We thank Getting Big Too Fast: Strategic Dynamics with Increasing Returns and Bounded Rationality

Abstract Neoclassical models of strategic behavior have yielded many insights into competitive behavior, despite the fact that they often rely on a number of assumptions-including instantaneous market clearing and perfect foresight-that have been called into question by a broad range of research. Researchers generally argue that these assumptions are &quot;good enough&quot; to predict an industry&apos;s probable equilibria, and that disequilibrium adjustments and bounded rationality have limited competitive implications. Here we focus on the case of strategy in the presence of increasing returns to highlight how relaxing these two assumptions can lead to outcomes quite different from those predicted by standard neoclassical models. Prior research suggests that in the presence of increasing returns, tight appropriability and accommodating rivals, in some circumstances early entrants can achieve sustained competitive advantage by pursuing Get Big Fast (GBF) strategies: rapidly expanding capacity and cutting prices to gain market share advantage and exploit positive feedbacks faster than their rivals. Using a simulation of the duopoly case we show that when the industry moves slowly compared to capacity adjustment delays, boundedly rational firms find their way to the equilibria predicted by conventional models. However, when market dynamics are rapid relative to capacity adjustment, forecasting errors lead to excess capacity, overwhelming the advantage conferred by increasing returns. Our results highlight the risks of ignoring the role of disequilibrium dynamics and bounded rationality in shaping competitive outcomes, and demonstrate how both can be incorporated into strategic analysis to form a dynamic, behavioral game theory amenable to rigorous analysis