Money and Macrodynamics

Alfred Eichner and Post-Keynesian Economics

Marc Lavoie, Louis-Philippe Rochon, Mario Seccareccia

• Publisher: Routledge
• Year: 2015
• ISBN:
  9781317464471
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

Alfred Eichner's pioneering contributions to post-Keynesian econmics offered significant insights on the way modern economies and institutions actually work. Published in 1987, his "Macrodynamics of Advanced Market Economies" contains rich chapters on dynamics and growth, investment, finance and income distribution, a timely chapter on the State and fiscal policy, and two analytical chapters on endogenous money that are years ahead of their time. Featuring chapters by many of Eichner's disciples, this book celebrates his rich contributions to post-Keynesian economics, and demonstrates that his work is in many ways as valid today as it was over two decades ago.

Part I

The Link Between Micro and Macro

1

Was Alfred Eichner a System Dynamicist?

Michael J. Radzicki

Was Alfred Eichner a system dynamicist? The short answer is no. From the available evidence, which includes his writings and recollections by his students and colleagues, Eichner never utilized system dynamics and possibly never even knew of its existence. Yet the available evidence also shows that Eichner approached economic problems like a system dynamicist and, in his magnum opus, The Macrodynamics of Advanced Market Economies, put forth an argument about the proper way to conduct macroeconomic analysis that would be well received within the system dynamics community.¹ The purpose of this chapter is to lay out the case that Alfred Eichner was a system dynamicist in spirit, if not in practice, and to argue that his exemplary work can be profitably extended via the use of system dynamics computer simulation modeling.

What Is System Dynamics?

System dynamics is a computer modeling technique originally developed by Jay W. Forrester at the Massachusetts Institute of Technology for the purpose of simulating socioeconomic systems in a realistic manner.² Forrester, a control engineer, pioneer in digital computing, and director of multiple large-scale engineering projects, created a tool and a way of thinking about socioeconomic problems that combined the things he knew best: feedback control theory, organizational behavior, and digital simulation. Forrester’s basic idea was that the decision rules followed by individual actors in a complex feedback system, along with the system’s physical, financial, social, and institutional structures, could be identified and coded into a system dynamics model. A digital computer could then be used to reveal the dynamic consequences of the interacting feedback processes. The resulting model could be used to explain why the system was behaving as it was and to design policies (i.e., changes to the system’s structure) that would improve the system’s performance.

Problems, Not Systems

Properly undertaken, system dynamics modeling is a problem-based, rather than a system-based process. That is, instead of modeling systems, system dynamicists identify and model problems from a systems perspective. Experience has shown that attempting to model systems rather than problems typically results in excessively large models that are difficult to understand and that do not yield insights into the fundamental causes of poor system behavior.

The system dynamics modeling process begins with a statement of the problem being experienced by the system under study. Although the problem can always be stated both verbally and in writing, it is also expressed pictorially by a set of time series graphs of important system variables called a reference mode. These graphs depict measured time series data and/or hand-drawn time shapes of important system variables assembled from written descriptions of the system’s behavior and/or from interviews with and/or observations of system experts and participants. The time paths are analyzed both in isolation and in relation to one another.

Circular and Cumulative Causation

Once the problem has been articulated and its associated time paths specified, a system dynamicist will begin searching for the stocks and flows responsible for generating the problematic behavior. Stocks are conceptualized as bathtubs and flows are conceptualized as pipe and faucet assemblies that fill and/or drain the tubs.³ From a system dynamics perspective, the process of flows filling and draining stocks creates all dynamic behavior in the world, be it in a physical, biological, financial or social system.⁴

A system’s stocks and flows do not exist in isolation, however. They are part of interconnected networks of feedback loops. Feedback is the transmission and return of information—information about how much “stuff” has accumulated in each of a system’s stocks. This information flows throughout a system and eventually returns to the pipe and faucet assemblies that fill or drain the stocks, thus closing the system’s feedback loops. Generally speaking, the information being transmitted via a system’s feedback loops is used by the agents in the model to make decisions that cause the pipe and faucet assemblies to open wider, open less, remain constant, or shut down completely.⁵

Two types of feedback loops exist in system dynamics models: positive loops and negative loops. Positive loops, which represent self-reinforcing processes such as the Keynesian-Kahnian multiplier or a wage-price spiral, usually destabilize systems by causing them to move away from their current state. In other words, positive loops are responsible for the (exponential) growth and decline of systems.⁶ Anything that can be described as a vicious or virtuous circle is a positive loop, as are economic processes such as speculation, bandwagon effects, increasing returns, and path dependency.

Negative feedback loops, which represent goal-seeking processes such as homeostatic mechanisms and many types of purposeful behavior, attempt to stabilize systems by working to keep them in their current state.⁷ If the corrective action they generate is significantly delayed (by stocks), however, they also can destabilize systems by causing them to oscillate.⁸ Economic processes such as macroeconomic cycles and spot market clearing behavior are generated by negative feedback loops.

From a system dynamics point of view, a system’s positive and negative feedback loops, within which are embedded its stocks and flows, fight for dominance or control of its dynamic behavior. This perspective is in complete harmony with much of post-Keynesian and institutional economics, in which the process of circular and cumulative causation is seen as the fundamental driving force behind the evolution of economic systems.⁹ It also has enormous implications for economic policy. If humans exhibit goal-seeking behavior, especially when the goals they seek are incompatible, systems can exhibit policy resistance and counterintuitive behavior. Leverage points (i.e., places where policy interventions can change the dynamics of a system in a positive manner) can be very difficult to locate, and systems can get “worse before better” or “better before worse” in response to policy changes. The separation of cause and effect in time (due to delays caused by stocks) and space, moreover, as well as human cognitive limitations, can make diagnosing the most effective changes in economic policy extremely challenging (Sterman 2000, Chapter 1).

Endogenous Point of View

In system dynamics modeling, explanations for problems are given in terms of the dominant feedback loops that are responsible for the behavior of the system.¹⁰As such, the explanations are endogenous. Indeed, in system dynamics modeling, exogenous variables that can significantly influence (drive) a system’s behavior are avoided whenever possible.¹¹

The desire to derive endogenous explanations means that system dynamicists usually create models with broader boundaries and longer time horizons than is typical in traditional economic modeling. It also means that system dynamicists must integrate knowledge from multiple disciplines and constituencies into their explanations (Sterman 1992, 13). The endogenous point of view thus requires the adoption of a holistic perspective and the belief that economics is truly a social science.

Actual Human Decision-Making

Generally speaking, the flow equations in a system dynamics model represent the actual decision-making rules utilized by the agents in the system in all of their (bounded) rational or irrational glory. These rules usually lead to disequilibrium behavior, which is crucial because actual systems rarely, if ever, exist in a state of equilibrium.¹² For example, in a system dynamics model it is quite common to represent what happens when the actual state of the system differs from an agent’s desired state or when an agent’s expectations are incorrect. As Sterman notes,

modeling disequilibrium behavior in human systems requires explicit separation and representation of actual, perceived, and desired states. [System dynamicists] must study and model processes of perception, information gathering, and goal formation. Such study nearly always involves field work, qualitative data, soft variables, and other techniques more suited to the ethnographer than the econometrician … To mimic the behavior of a system properly the decision rules in [system dynamics] models must capture the information cues, pressures and constraints which condition actual managerial action, warts and all. This often leads to models of bounded rationality, to representations of the heuristics, routines, and rules of thumb a decision maker or organization uses to simplify complex decision tasks. But it can also include emotional pressures and other non-cognitive dimensions. (Sterma...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Introduction: Alfred Eichner and the State of Post-Keynesian Economics
• Part I. The Link Between Micro and Macro
• Part II. Competition and the Globalized World
• Part III. Credit, Money, and Central Banking
• About the Editors and Contributors
• Index