An Electro-Analog Method for Investigating Problems in Economic Dynamics: Inventory Oscillations

Morehouse, N.F.; Strotz, Robert H.; Horwitz, S. J.

doi:10.2307/1907832

Cited by 38 publications

(15 citation statements)

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“…During the fifties, the well-known economist A. W. Phillips -who first recovered the so-called Phillips curve (i.e., the inverse relationship between unemployment and inflation rate)-used a hydraulic machine for simulation of macroeconomic processes (Phillips [150], see also [146]). Even earlier, we can find simulations via electronic circuits published in economics journals (Morehouse et al [143]). …”

Section: Overviewmentioning

confidence: 97%

Agent-based models of financial markets

et al. 2007

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This review deals with several microscopic ("agent-based") models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, ContBouchaud, Solomon-Weisbuch, Lux-Marchesi, Donangelo-Sneppen and Solomon-Levy-Huang. After an overview of simulation approaches in financial economics, we first give a summary of the DonangeloSneppen model of monetary exchange and compare it with related models in economics literature. Our selective review then outlines the main ingredients of some influential early models of multi-agent dynamics in financial markets (Kim-Markowitz, Levy-Levy-Solomon). As will be seen, these contributions draw their inspiration from the complex appearance of investors' interactions in real-life markets. Their main aim is to reproduce (and, thereby, provide possible explanations) for the spectacular bubbles and crashes seen in certain historical episodes, but they lack (like almost all the work before 1998 or so) a perspective in terms of the universal statistical features of financial time series. In fact, awareness of a set of such regularities (power-law tails of the distribution of returns, temporal scaling of volatility) only gradually appeared over the nineties. With the more precise description of the formerly relatively vague characteristics ( e.g. moving from the notion of fat tails to the more concrete one of a power-law with index around three), it became clear that financial markets dynamics give rise to some kind of universal scaling laws. Showing similarities with scaling laws for other systems with many interacting sub-units, an exploration of financial markets as multi-agent systems appeared to be a natural consequence. This topic was pursued by quite a number of contributions appearing in both the physics and economics literature since the late nineties. From the wealth of different flavors of multi-agent models that have appeared by now, we discuss the ContBouchaud, Solomon-Levy-Huang and Lux-Marchesi models. Open research questions are discussed in our concluding section.

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Section: Overviewmentioning

confidence: 97%

Agent-based models of financial markets

et al. 2007

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“…The following theorem establishes a converse of Theorem 1 by assuming, as well as (7), that the element is reciprocal.…”

Section: Le Chatelier's Principlementioning

confidence: 93%

“…Theorem 1 is also true if c is subject to constraints that do not involve u ; these can be included in the theorem by appropriate restrictions on the set A . Note that the inequality (7) becomes strict if M(u) is a singleton for all u .…”

Section: U1 Ebu2ebc1en(c2u1)c~en(c'u')mentioning

confidence: 99%

Network methods in models of production

Watson

1980

Networks

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The author explores the ground common to the theories of electrical networks and economic networks by generalizing the notion of a Kirchhoff network of multiterminal elements to that of a Leontieff network of multiterminal elements. In this context the author introduces Le Chatelier's principle and proves a converse t o it, derives a generalization of Tellegen's theorem, and discusses the inheritance by a composite element of the properties of its constituent elements. Two types of maximizing firms are then treated as multiterminal elements; the preceding theory is applied to them and to composite firms constructed from them.

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“…While it had been repeatedly pointed out that Walras and Pareto were deeply influenced by the success of Newtonian physics (Ingrao and Israel, 2000), later analogies between physical modeling and economic phenomena have had more of an engineering flavor, e.g., Phillips' hydraulic apparatus for the simulation of macroeconomic dynamics (Phillips, 1950), or the use of electronic circuits for the same purpose in Morehouse et al (1950) and Enke (1951). With the introduction of dynamic optimization for representative agent models the link between the economics and engineering literature became even stronger.…”

Section: Introduction To Special Issue On 'Applications Of Statisticamentioning

confidence: 99%