Oscillations and Amplifications in Demand-Supply Network Chains

Oscillations and Amplifications in Demand-Supply Network Chains


From Modeling and Measuring the Bullwhip Effect

Demand variability and uncertainty is a driver of supply chain inventory. Managing supply chains can be a challenge when demand variability and uncertainty is high. For a company in a supply chain consisting of multiple stages, each of which is run by a separate organization (or company), the variability of demand faced by this company can be much higher than the variability faced by downstream stages (where “downstream stages” refers to the stages closer to the final consumption of the product). The bullwhip effect refers to the phenomenon where demand variability amplifies as one moves upstream in a supply chain (Lee et al, 1997a, or LPW). LPW described this as a form of demand information distortion. Lee et al (1997b) further discussed the managerial and practical aspects of the bullwhip effect, giving more industry examples. The bullwhip effect phenomenon is closely related to studies in systems dynamics (Forrester, 1961; Sterman, 1989; Senge, 1990). Sterman (1989) observed a systematic pattern of demand variation amplification in the Beer Game, and attributed it to behavioral causes (i.e., misperceptions of feedback). Macroeconomists have also studied the phenomenon (Holt et al, 1968; Blinder, 1981; Blanchard, 1983).


From Operational and Behavioral Causes of Supply Chain Instability


Supply chain instability is often described as the bullwhip effect, the tendency for variability to increase at each level of a supply chain as one moves from customer sales to production (Lee et al. 1997, Chen et al. 2000). While amplification from stage to stage is important, supply chain instability is a richer and more subtle phenomenon. The economy, and the networks of supply chains embedded within it, is a complex dynamic system and generates multiple modes of behavior. These include business cycles (oscillation), amplification of orders and production from consumption to raw materials (the bullwhip), and phase lag (shifts in the timing of the cycles from consumption to materials). High product returns and spoilage are common in industries from consumer electronics to hybrid seed corn (Gonçalves 2003). Many firms experience pronounced hockey-stick patterns in which orders and output rise sharply just prior to the end of a month or quarter as the sales force and managers rush to hit revenue goals. Boom and bust dynamics in supply chains are often worsened by phantom orders—orders customers place in response to perceived shortages in an attempt to gain a greater share of a shrinking pie (T. Mitchell 1923, Sterman 2000, ch. 18.3, Gonçalves 2002, Gonçalves and Sterman 2005).

What are the causes of supply chain instability? Why does supply chain instability persist, despite the lean revolution and tremendous innovations in technology? What can be done to stabilize supply chains and improve their efficiency?

Here I describe the origins of supply chain instability from a complex systems perspective. The dynamics of supply chain networks arise endogenously from their structure. That structure includes both operational and behavioral elements.


From Operational and Behavioral Causes of Supply Chain Instability

Oscillation, Amplification, and Phase Lag

Exhibit 1 shows industrial production in the US. The data exhibit several modes of behavior. First, the long-run growth rate of manufacturing output is about 3.4%/year. Second, as seen in the bottom panel, production fluctuates significantly around the growth trend. The dominant periodicity is the business cycle, a cycle of prosperity and recession of about 3–5 years in duration, but exhibiting considerable variability.

The amplitude of business cycle fluctuations in materials production is significantly greater than that in consumer goods production (exhibit 2). The peaks and troughs of the cycle in materials production also tend to lag behind those in production of consumer goods. Typically, the amplitude of fluctuations increases as they propagate from the customer to the supplier, with each upstream stage tending to lag behind its customer. These three features, oscillation, amplification, and phase lag, are pervasive in supply chains.


From Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

A central question in operations management is whether the oscillations, amplification and phase lag observed in supply chains arise as the result of operational or behavioral causes.

Operational theories assume that decision makers are rational agents who make optimal decisions given their local incentives and information.  Supply chain instability must then result from the interaction of rational actors with the physical and institutional structure of the system.

  • Physical structure includes the network linking customers and suppliers and the placement of inventories and buffers within it, along with capacity constraints and time delays in production, order fulfillment, transportation, and so on.
  • Institutional structure includes the degree of horizontal and vertical coordination and competition among firms, the availability of information to decision makers in each organization, and the incentives faced by each decision maker.

Behavioral explanations also capture the physical and institutional structure of supply chains, but view decision makers as boundedly rational actors with imperfect mental models, actors who use heuristics to make ordering, production, capacity acquisition, pricing and other decisions (Morecroft 1985, Sterman 2000, Boudreau et al. 2003, Gino & Pisano 2008, Bendoly et al. 2010, Croson et al. 2013).

Amplifications and Phase Lag

Amplification and phase lag arise from the presence of basic physical structures including stocks of inventory and delays in adjusting production or deliveries to changes in incoming orders.


Oscillations, however, are not inevitable. They arise from boundedly rational, behavioral decision processes

The difference matters: if supply chain instability arises from operational factors and rational behavior, then policies must be directed at changing the physical and institutional structure of the system, including incentives.

If, however, instability arises from bounded rationality and emotional arousal such policies may not be sufficient.


Key People:

  • Jay W Forrester
  • John Sterman
  • Rogelio Oliva
  • Hau L Lee


Key Terms:

  • Bullwhip Effect
  • Oscillations
  • Amplifications
  • Negative Feedback Loop
  • Positive Feedback Loop
  • Phase Lag
  • Supply Chain Networks
  • Inventory Management
  • Production Smoothening
  • Beer Distribution Game
  • Industrial Dynamics
  • Operational and Institutional Structures
  • Behavioral causes
  • Instability
  • Variability
  • Uncertainty


Key Sources of Research:


Behavioral Causes of Demand Amplification in Supply Chains: “Satisficing” Policies with Limited Information Cues

Rogelio Oliva

Paulo Gonçalves


Click to access 1889_087eb4f1-0532-4a7d-a206-3d565efc02af_2005-OLIVA133.pdf




Francisco Campuzano Bolarín1,Lorenzo Ros Mcdonnell1, Juan Martín García



Click to access CAMPU215.pdf



The impact of order variance amplification/dampening on supply chain performance


Robert N. Boute, Stephen M. Disney, Marc R. Lambrecht and Benny Van Houdt


Click to access KBI_0603.pdf



Coping with Uncertainty: Reducing ”Bullwhip” Behaviour in Global Supply Chains


Rachel Mason-Jones, and Denis R. Towill

Click to access 24557527da7aa9da7de238fe7f4a463b2af6.pdf



Bullwhip in Supply Chains ~ Past, Present and Future

Steve Geary Stephen M Disney and Denis R Towill


Click to access 492a6e6ae1d0f186fe2570b7477428e8e467.pdf



Shrinking the Supply Chain Uncertainty Circle

R Mason-Jones

Click to access 19980901d.pdf




Gürdal Ertek, Emre Eryılmaz


Click to access ertek_eryilmaz_cels2008.pdf



Information distortion in a supply chain: The bullwhip effect

Hau L Lee; V Padmanabhan; Seugjin Whang

Management Science; Apr 1997; 43, 4;

Click to access f26117d56ab96aabe2d6cee4c390ab20ee18.pdf






Click to access 140687.pdf



The Bullwhip Effect in Supply Chains

Hau L. Lee, V. Padmanabhan and Seungjin Whang





The Bullwhip Effect: Analysis of the Causes and Remedies


Jonathan Moll

Rene Bekker


Click to access werkstuk-moll_tcm243-354834.pdf




Vinaya Shukla, Mohamed M Naim, Ehab A Yaseen





How human behaviour amplifies the bullwhip effect – a study based on the beer distribution game online

Joerg Nienhaus, Arne Ziegenbein*, Christoph Duijts


Click to access Bullwhip_Effect_Article.pdf



The Bullwhip Effect in Different Manufacturing Paradigm: An Analysis

Shamila Nabi KHAN1 Mohammad Ajmal KHAN2 Ramsha SOHAIL


Click to access 11.pdf.pdf



On replenishment rules, forecasting and the bullwhip effect in supply chains

Stephen M. Disney1 and Marc R. Lambrecht


Click to access Disney%20-%20On%20replenishment%20rules%20forecasting%20and%20the%20bullwhip%20effect%20in%20supply%20chains%20pre%20print.pdf



Causes and Remedies of Bullwhip Effect in Supply Chain

Sivakumar Balasubramanian Larry Whitman Kartik Ramachandran Ravindra Sheelavant


Click to access 2001IERCBullwhip.pdf



Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

John Sterman


Click to access Sterman%20Beh%20Ops%20Handbook%20Chapter%20140210.pdf



Modeling and Measuring the Bullwhip Effect

Li Chen and Hau L. Lee


Click to access Chen_Lee_Bullwhip_2015.pdf



Operational and Behavioral Causes of Supply Chain Instability

John D. Sterman

Click to access 2a3118c5c7d2bd475335549b0b943009d66c.pdf



Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

Rachel Croson, Karen Donohue, Elena Katok, and John Sterman

Click to access Order%20Stability%20in%20SCs050212.pdf

Click to access Order_Stability_070505.pdf




John D. Sterman

Click to access E6-63-01-02.pdf



When Do Minor Shortages Inflate To Great Bubbles?

Paulo Gonçalves



Click to access Gonca1.pdf



A new technology paradigm for collaboration in the supply chain

Branko Pecar and Barry Davies

Click to access c522d454d1dc036a22db29b2dee005dbc44e.pdf




Frank Chent, Zvi Drezner2 , Jennifer K. Ryan3 and David Simchi-Levi


Click to access 4%20chen.pdf



Supply and Production Networks: From the Bullwhip Effect to Business Cycles

Dirk Helbing Stefan Laemmer



Click to access 04-12-033.pdf



Inventory dynamics and the bullwhip effect : studies in supply chain performance

Udenio, M.



Click to access 776508.pdf







Click to access w1503.pdf






Victor Zarnowitz

WorkingPaper7010 htp:/w.nber.org/papers/w7010



Click to access w7010.pdf



The Beginning of System Dynamics

Jay W. Forrester


Click to access D-4165-1.pdf



Profiles in Operations Research: Jay Wright Forrester

David C. Lane John D. Sterman


Click to access jwf-profile-in-op.pdf






Click to access 54fe11ea0aaa47f4c8e08959be2ef52d50a6.pdf






Click to access Forrester68.pdf



Industrial Dynamics

Jay W Forrester




Business Dynamics

John Sterman



Increasing Returns, Path Dependence, Circular and Cumulative Causation in Economics

Increasing Returns, Path dependence, and Circular and Cumulative Causation in Economics


Increasing Returns is another term for Positive Feedback loop.

Path Dependence is also known as Lock-In

Circular and Cumulative Causation is another name for Positive Feedback Loop.


Vicious Circle – Bad gets to worse, Failure leads to more failure

Virtuous Circle – Success breeds Success, Wealth gets more wealth


See this Document – Book Foreward by Geoffrey Hodgson

Geoffrey Hodgson foreward in a book The Foundations of Non Equilibrium Economics.


Key Economists :

  • Allyn Young
  • Gunnar Myrdal
  • Karl William Kapp
  • Kaldor
  • Veblen
  • Paul Romer
  • Knut Wicksell
  • W. Brian Arthur
  • Paul David
  • Steven Durlauf
  • Nicholas Georgescu-Roegen


Various Increasing Returns, Circular and Cumulative Causations Theories

  • CCC Theory of Allyn Young
  • CCC Theory of N Kaldor
  • CCC Theory of Gunnar Myrdal
  • CCC Theory of T Veblen
  • CCC Theory of Paul Romer
  • CCC Theory of Paul Krugman
  • CCC Theory of W. Brain Arthur


Circular Causation in Kaldor Theory




Key Sources of Research:


A A Young

Increasing Returns and Technical Progress

The Economic Journal


Click to access young28.pdf



Kaldor, N. (1966)

Causes of the Slow Rate of Economic Growth of the United Kingdom,

Cambridge: Cambridge University Press.



Kaldor, N. (1970)

“The case for regional policies,”

Scottish Journal of Political Economy, 17, pp. 337-348.



Kaldor, N. (1985)

Economics Without Equilibrium,

Cardiff, University College Cardiff Press.



Kaldor, N. (1996)

Causes of Growth and Stagnation in the World Economy,

Cambridge, Cambridge University Press




Kaldor, N., 1981.

The role of increasing returns, technical progress and cumulative causation in the theory of international trade and economic growth.

Economie appliquée, 34(4), pp.593-617.



Nicholas Kaldor on Endogenous Money and Increasing Returns

Guglielmo Forges Davanzati



Click to access Forges.pdf



Fujita, Nanako.

“Myrdal’s theory of cumulative causation.”

Evolutionary and Institutional Economics Review 3, no. 2 (2007): 275-284.



Gunnar Myrdal’s Theory of Cumulative Causation Revisited

Nanako Fujita


Click to access paper147.pdf



Circular Cumulative Causation (CCC) à la Myrdal and Kapp — Political Institutionalism for Minimizing Social Costs

Sebastian Berger




European Contributions to Evolutionary Institutional Economics: The Cases of ‘Cumulative Circular Causation’ (CCC) and ‘Open Systems Approach’ (OSA).
Some Methodological and Policy Implications

Sebastian Berger and Wolfram Elsner


Click to access European_Institutionalism_Berger_Elsner_JEI_No_2_07_5_07.pdf



Dutt, Amitava Krishna.

“Path dependence, equilibrium and economic growth.”

In Path Dependency and Macroeconomics, pp. 119-161. Palgrave Macmillan UK, 2009.



Setterfield, Mark.

“Notes and comments. Cumulative causation, interrelatedness and the theory of economic growth: a reply to Argyrous and Toner.”

Cambridge Journal of Economics 25, no. 1 (2001): 107-112.



Setterfield, Mark.

“‘History versus equilibrium’and the theory of economic growth.”

Cambridge Journal of Economics 21, no. 3 (1997): 365-378.



Setterfield, M. (2009)

“Path dependency, hysteresis and macrodynamics,”

in P. Arestis and M. Sawyer (eds) Path Dependency and Macroeconomics (International Papers in Political Economy 2009), London, Palgrave Macmillan, 37-79



Setterfield, M.


Rapid Growth and Relative Decline: Modelling Macroeconomic Dynamics with Hysteresis,

London: Macmillan.




Kaldor’s 1970 Regional Growth Model Revisited





Argyrous, George.

“Setterfield on cumulative causation and interrelatedness: a comment.”

Cambridge Journal of Economics 25, no. 1 (2001): 103-106.



Endogenous Growth: A Kaldorian Approach

Mark Setterfield





Increasing Returns and Long Run Growth

Paul Romer




O’Hara, P.A., 2008.

Principle of circular and cumulative causation: Fusing Myrdalian and Kaldorian growth and development dynamics.

Journal of Economic Issues, 42(2), pp.375-387.



Path Dependency and Macroeconomics

edited by P. Arestis, Malcolm Sawyer



Main Currents in Cumulative Causation: The Dynamics of Growth and Development
Phillip Toner
Palgrave Macmillan UK, May 12, 1999 – Business & Economics – 228 pages




Why is Economics not an Evolutionary Science?

Thorstein Veblen

(with an introduction by Jean Boulton)






On the evolution of Thorstein Veblen’s evolutionary economics

Geoffrey M. Hodgson


Click to access evveblenec.pdf




“Different epistemologies beneath similar methods: The case of causal loop thinkers.”

Maruyama, Magoroh.

Human Systems Management 9, no. 3 (1990): 195-198.




The feedback concept in American social science, with implications for system dynamics.

Richardson, G.

(1983, July).

Click to access richa001.pdf



Path Dependence in Aggregate Output



Click to access 9870fa9010ed42d897dae442a3316d5cf805.pdf



Nonergodic Economic Growth



Click to access Nonergodic%20Economic%20Growth.pdf



Evolution and Path Dependence in Economic Ideas: Past and Present

edited by Pierre Garrouste, Stavros Ioannides,

European Association for Evolutionary Political Economy



Path dependence, its critics and the quest for ‘historical economics

Paul A. David

Click to access swp00011.pdf



Positive Feedbacks and Research Productivity in Science: Reopening Another Black Box

Paul A. David


Click to access David(1994)_PositiveFeedbacks_Marstrand3_re-release%5B1%5D.20070702.120531.pdf



Increasing Returns and Path Dependence in the Economy

By W. Brian Arthur

University of Michigan Press, 1994 – Business & Economics – 201 pages



Positive Feedbacks in the Economy

W. Brian Arthur

26 November 1989





Complexity economics: a different framework for economic thought

W. Brian Arthur

March 12, 2013


Click to access Comp.Econ.SFI.pdf



A webpage for resources on Path dependence in Economics




The Foundations of Non-Equilibrium Economics: The Principle of Circular and Cumulative Causation

edited by Sebastian Berger



The New Approach to Regional Economics Dynamics: Path Dependence and Spatial Self-Reinforcing Mechanisms

Domenico Marino and Raffaele Trapasso


Click to access Chapter%2015%20The%20New%20Approach%20to%20Regional%20Economics%20Dynamics;%20Path%20Dependence%20and%20Spatial%20Self-Reinforcing%20Mechanisms.pdf


Positive Feedback Mechanisms in. Economic Development: A Review of Recent Contributions


Click to access Positive%20Feedback%20Mechanisms%20in%20Economic%20Development.pdf



Increasing Returns and Economic Geography

Paul Krugman


March 4, 2010


Click to access krugman91.pdf

Feedback Thought in Economics and Finance

Feedback Thought in Economics and Finance

  • Negative Feedbacks
  • Positive Feedbacks
  • Stocks and Flows
  • Limiting Factors


Key People:

  • Jay Forrester
  • George Richardson
  • John Sterman
  • Michael Radzicki
  • Mikhail Oet
  • Oleg Pavlov
  • Eric D. Beinhocker
  • Stuart A. Umpleby
  • Khalid Saeed
  • Kaoru Yamaguchi


Reflexivity and Second order economics are closely related concepts.


From System Dynamics and Its Contribution to Economics and Economic Modeling


System dynamics is a computer simulation modeling methodology that is used to analyze complex nonlinear dynamic feedback systems for the purposes of generating insight and designing policies that will improve system performance. It was originally created in 1957 by Jay W. Forrester of the Massachusetts Institute of Technology as a methodology for building computer simulation models of problematic behavior within corporations. The models were used to design and test policies aimed at altering a corporation’s structure so that its behavior would improve and become more robust.

Today, system dynamics is applied to a large variety of problems in a multitude of academic disciplines, including economics. System dynamics models are created by identifying and linking the relevant pieces of a system’s structure and simulating the behavior generated by that structure. Through an iterative process of structure identification, mapping, and simulation a model emerges that can explain (mimic) a system’s problematic behavior and serve as a vehicle for policy design and testing. From a system dynamics perspective a system’s structure consists of stocks, flows, feedback loops, and limiting factors.

Stocks can be thought of as bathtubs that accumulate/de-cumulate a system’s flows over time. Flow can be thought of as pipe and faucet assemblies that fill or drain the stocks. Mathematically, the process of flows accumulating/de-cumulating in stocks is called integration. The integration process creates all dynamic behavior in the world be it in a physical system, a biological system, or a socioeconomic system. Examples of stocks and flows in economic systems include a stock of inventory and its inflow of production and its outflow of sales, a stock of the book value of a firm’s capital and its inflow of investment  spending and its outflow of depreciation, and a stock of employed labor and its inflow of hiring and its outflow of labor separations.

Feedback is the transmission and return of information about the amount of information or material that has accumulated in a system’s stocks. Information travels from a stock back to its flow(s) either directly or indirectly, and this movement of information causes the system’s faucets to open more, close a bit, close all the way, or stay in the same place. Every feedback loop has to contain at least one stock so that a simultaneous equation situation can be avoided and a model’s behavior can be revealed recursively. Loops with a single stock are termed minor, while loops containing more than one stock are termed major. 

Two types of feedback loops exist in system dynamics modeling: positive loops and negative loops. Generally speaking, positive loops generate self-reinforcing behavior and are responsible for the growth or decline of a system. Any relationship that can be termed a virtuous or vicious circle is thus a positive feedback loop. Examples of positive loops in economic systems include path dependent processes, increasing returns, speculative bubbles, learning by-doing, and many of the relationships found in macroeconomic growth theory. Forrester [12], Radzicki and Sterman [46],Moxnes [32], Sterman (Chap. 10 in [55]), Radzicki [44], Ryzhenkov [49], and Weber [58] describe system dynamics models of economic systems that possess dominant positive feedback processes.

Negative feedback loops generate goal-seeking behavior and are responsible for both stabilizing systems and causing them to oscillate. When a negative loop detects a gap between a stock and its goal it initiates corrective action aimed at closing the gap. When this is accomplished without a significant time delay, a system will adjust smoothly to its goal. On the other hand, if there are significant time lags in the corrective actions of a negative loop, it can overshoot or undershoot its goal and cause the system to oscillate. Examples of negative feedback processes in economic systems include equilibrating mechanisms (“auto-pilots”) such as simple supply and demand relationships, stock adjustment models for invetory control, any purposeful behavior, and many of the relationships found in macroeconomic business cycle theory. Meadows [27], Mass [26], Low [23], Forrester [12], and Sterman [54] provide examples of system dynamics models that generate cyclical behavior at the macro-economic and micro-economic levels.

From a system dynamics point of view, positive and negative feedback loops fight for control of a system’s behavior. The loops that are dominant at any given time determine a system’s time path and, if the system is nonlinear, the dominance of the loops can change over time as the system’s stocks fill and drain. From this perspective, the dynamic behavior of any economy that is, the interactions between the trend and the cycle in an economy over time can be explained as a fight for dominance between the economy’s most significant positive and negative feedback loops.


Key Sources of Research:


Systemic Financial Feedbacks – Conceptual Framework and Modeling Implications

Dieter Gramlich1 and Mikhail V. Oet

Click to access 54992d5c0cf2519f5a1df20b.pdf




Mikhail V. Oet

Oleg V. Pavlov

Click to access P1441.pdf



Mr. Hamilton, Mr. Forrester, and a Foundation for Evolutionary Economics

Michael J. Radzicki


Click to access 0a85e52e41951a468c000000.pdf


European Contributions to Evolutionary Institutional Economics: The Cases of ‘Cumulative Circular Causation’ (CCC) and ‘Open Systems Approach’ (OSA).
Some Methodological and Policy Implications


Sebastian Berger and Wolfram Elsner



System Dynamicsand Its Contribution to Economics and Economic Modeling



Click to access 02e7e53331fe5f394b000000.pdf



Institutional Economics, Post Keynesian Economics, and System Dynamics: Three Strands of a Heterodox Economics Braid

Michael J. Radzicki, Ph.D.

Click to access 02e7e53331eeea388c000000.pdf



Was Alfred Eichner a System Dynamicist?


Michael J. Radzicki

Click to access 0f317536d3f41a13fb000000.pdf


Second-Order Economics as an Example of Second-Order Cybernetics

Stuart A. Umpleby


Click to access 890.pdf


Reflexivity, complexity, and the nature of social science

Eric D. Beinhocker


Click to access Beinhocker%20(JEM%202013).pdf


Path dependence, its critics and the quest for ‘historical economics’


Paul A. David

Click to access 0deec53b482217c114000000.pdf


Endogenous Feedback Perspective on Money in a Stock-Flow Consistent Model

I. David Wheat
University of Bergen


Click to access Wheat%20Endogenous%20Feedback%20Perspective%20on%20Money%20WP.pdf


Classical Economics on Limits to Growth

Khalid Saeed


Click to access Classical%20Economics%20on%20Limits%20to%20Growth.pdf



Misperceptions of Feedback in Dynamic Decisionmaking

John D. Sterman


Click to access 54359e4e0cf2bf1f1f2b3520.pdf


Learning in and about complex systems

John D. Sterman


Click to access sterman-learning-in-and-about-complex-systems.pdf


Micro-worlds and Evolutionary Economics

Michael J. Radzicki

Click to access radzi533.pdf


Feedback Thought in Social Science and Systems Theory

George Richardson

Pegasus Communications, Inc. ©1999


The Feedback concept in American Social Sciences 

George Richardson


Click to access richa001.pdf


Evolutionary Economics and System Dynamics

Radzicki and Sterman


Effects of Feedback Complexity on Dynamic Decision Making
Ernst Diehl, John D. Sterman

Organizational Behavior and Human Decision Processes

Volume 62, Issue 2, May 1995, Pages 198-215


Old Wine in a New Bottle:
Towards a Common Language for Post-Keynesian Macroeconomics Model

Ginanjar Utama


Click to access P1307.pdf


On Component Based Modeling Approach using System Dynamics for The Financial System (With a Case Study of Keen-Minsky Model)

Ginanjar Utama


Click to access P1209.pdf


On the Monetary and Financial Stability under A Public Money System

– Modeling the American Monetary Act Simplified –

Kaoru Yamaguchi


Click to access P1065.pdf


Integration of Real and Monetary Sectors with Labor Market
– SD Macroeconomic Modeling (3) –

Kaoru Yamaguchi



Balance of Payments and Foreign Exchange Dynamics

– SD Macroeconomic Modeling (4) –

Kaoru Yamaguchi, Ph.D


Click to access YAMAG211.pdf



Money and Macroeconomic Dynamics

Accounting System Dynamics Approach

Kaoru Yamaguchi, Ph.D


Click to access Macro%20Dynamics.pdf


Does Money Matter on the Formation of Business Cycles and Economic Recessions ?
– SD Simulations of A Monetary Goodwin Model –


Kaoru Yamaguchi

Click to access DBS12-01.pdf


Head and Tail of Money Creation and its System Design Failures

– Toward the Alternative System Design –

JFRC Working Paper No. 01-2016

Kaoru Yamaguchi, Ph.D.

Yokei Yamaguchi

Click to access Head-and-Tail-2016_WP__-_Japan_Futures_Research_Center.pdf


Modelling the Great Transition


Emanuele Campiglio

New Economics Foundation

Click to access Emanuel-SD-conference-9-2-12.pdf


The role of System Dynamics modelling to understand food chain complexity and address challenges for sustainability policies

Irene Monasterolo1, Roberto Pasqualino, Edoardo Mollona


Click to access CFP3-06_Full_Paper.pdf


Dynamic regional economic modeling: a systems approach

I. David Wheat



Click to access 1.17_wheat_pawluczuk.pdf


Expectation Formation and Parameter Estimation in Uncertain Dynamical Systems: The System Dynamics Approach to Post Keynesian-Institutional Economics



Michael J. Radzicki


Click to access 0deec536d3da974962000000.pdf


The Circular and Cumulative Structure of Administered Pricing

Mark Nichols, Oleg Pavlov, and Michael J. Radzicki


Click to access 02e7e5282d33c933df000000.pdf


A System Dynamics Approach to the Bhaduri‐Marglin Model

Klaus D. John

Click to access P1306.pdf


An Institutional Dynamics Model of the Euro zone crisis: Greece as an Illustrative Example

Domen Zavrl

Miroljub Kljajić

Click to access P1144.pdf


Is system dynamics modelling of relevance to neoclassical economists? 

Douglas J. Crookes Martin P. De Wit

Click to access 00b7d53861d6b14d9f000000.pdf


System dynamics modelling and simulating the effects of intellectual capital on economic growth

Ivona Milić Beran




Increasing Returns and Path Dependence in Economics

Increasing Returns and Path Dependence in the Economics

  • Increasing Returns
  • Self Reinforcement
  • Positive Feedbacks
  • Lock-in


Key People:

  • W. Brain Arthur
  • Ken Arrow
  • Scott Page
  • Paul David


From Increasing Returns,  Path Dependence in Economy 

Forward to the book by K Arrow

The concept of increasing returns has had a long but uneasy presence in economic analysis. The opening chapters of Adam Smith’s Wealth of Nations put great emphasis on increasing returns to explain both specialization and economic growth. Yet the object of study moves quickly to a competitive system and a cost-of-production theory of value, which cannot be made rigorous except by assuming constant returns. The English school (David Ricardo, John Stuart Mill) followed the competitive assumptions and quietly dropped Smith’s boldly-stated proposition that, “the division of labor is limited by the extent of the market,” division of labor having been shown to lead to increased productivity.


Other analysts in different traditions, especially the French mathematician and economist, A. A. Cournot (1838), saw clearly enough the incompatibility of increasing returns and perfect competition and developed theories of monopoly and oligopoly to explain the economic system implied by increasing returns. But this tradition acts like an underground river, springing to the surface only every few decades. Alfred Marshall expanded broadly, if vaguely, on the implications of increasing returns, including those for economic growth, irreversible supply curves, and the like, as well as the novel and far-reaching concept of externalities, where some, at least, of the increasing returns are captured, not by the producer but by others.


The implications of increasing returns for imperfect competition were developed, though far from completely, by Edward Chamberlin and Joan Robinson in the 1930s. There was sporadic emphasis on the role of increasing returns in economic growth by Allyn Young (1928) (but only in very general terms) and then by Nicholas Kaldor in the1950s. Many developmental theorists, particularly in the 1950s, advocated radical planning policies based on vague notions of increasing returns.


From Path Dependence


A survey of the literature on path dependence reveals four related causes: increasing returns, self-reinforcement, positive feedbacks, and lock-in. Though related, these causes differ. Increasing returns means that the more a choice is made or an action is taken, the greater its benefits. Self-reinforcement means that making a choice or taking an action puts in place a set of forces or complementary institutions that encourage that choice to be sustained. With positive feedbacks, an action or choice creates positive externalities when that same choice is made by other people. Positive feedbacks create something like increasing returns, but mathematically, they differ. Increasing returns can be thought of as benefits that rise smoothly as more people make a particular choice and positive feedbacks as little bonuses given to people who already made that choice or who will make that choice in the future. Finally, lock-in means that one choice or action becomes better than any other one because a sufficient number of people have already made that choice.


From Positive Feedbacks in the Economy


Conventional economic theory is built on the assumption of diminishing returns. Economic actions eventually engender a negative feedback that leads to a predictable equilibrium for prices and market shares. Negative feedback tends to stabilize the economy because any major changes will be offset by the very reactions they generate. The high oil prices of the 1970’s encouraged energy conservation and increased oil exploration, precipitating a predictable drop in prices by 198x. According to conventional theory the equilibrium marks the “best” outcome possible under the circumstances: the most efficient use and allocation of resources.

Such an agreeable picture often does violence to reality. In many parts of the economy stabilizing forces appear not to operate. Instead, positive feedback magnifies the effect of small economic shifts; the economic models that describe such effects differ vastly from the conventional ones. Diminishing returns imply a single equilibrium point for the economy, but positive feedback—increasing returns—make for multiple equilibrium points. There is no guarantee that the particular economic outcome selected from among the many alternatives will be the “best” one. Furthermore, once chance economic forces select a particular path, it may become locked in regardless of the advantages of other paths. If one product or nation in a competitive marketplace gets ahead by “chance” it tends to stay ahead and even increase its lead. Predictable, shared markets are no longer guaranteed.

If increasing-returns mechanisms are important, why have they been largely ignored until recently? Some would say that complicated products—high technology—for which increasing returns are so prevalent, are themselves a recent phenomenon. This is true, but only part of the answer. After all, in the 1940’s and 1950’s economists like Gunnar Myrdal and Nicholas Kaldor identified “cumulative causation” or positive feedback mechanisms that did not involve technology. Orthodox economists avoided increasing returns for deeper reasons.

Some economists found the existence of more than one solution to the same problem distasteful—unscientific. “Multiple equilibria,” wrote Josef Schumpeter in 1954, “are not necessarily useless, but from the standpoint of any exact science the existence of a uniquely determined equilibrium is, of course, of the utmost importance, even if proof has to be purchased at the price of very restrictive assumptions; without any possibility of proving the existence of uniquely determined equilibria—or at all events, of a small number of possible equilibria—at however high a level of abstraction, a field of phenomena is really a chaos that is not under analytical control.”

Other economists could see that increasing returns would destroy their familiar world of unique, predictable equilibria and along with this the notion that the market’s choice was always best. Moreover, if one or a few firms came to dominate a market, the assumption of perfect competition, that no firm is large enough to affect market prices on its own (which makes economic problems easy to analyze), would also be a casualty. When John Hicks surveyed these possibilities in 1939 he drew back in alarm. “The threatened wreckage,” he wrote, “is that of the greater part of economic theory.” Economists restricted themselves to diminishing returns, which presented no anomalies and could be analyzed completely.

Studying such problems in 1979, I believed I could see a way out of many of these difficulties. In the real world, if several similar-sized firms entered a market together, small fortuitous events—unexpected orders, chance meetings with buyers, managerial whims—would help determine which ones achieved early sales and, over time, which firm came to dominate. Economic activity is quantized by individual transactions that are too small to foresee, and these small “random” events could cumulate and become magnified by positive feedbacks over time to determine which solution was reached. This suggested that situations dominated by increasing returns should be modeled not as static, deterministic problems, but rather as dynamic processes with random events, and with natural positive feedbacks or non-linearities. With this strategy an increasing-returns market could be recreated theoretically and watched as its corresponding process unfolded again and again. Sometimes one solution would emerge, sometimes (under identical conditions) another. It would be impossible to know in advance which of the multiple solutions would emerge in any given run, but it would be possible to record the particular set of random events leading to each solution and to study the probability that a particular solution will emerge under a certain set of initial conditions. The idea was simple and it may well have occurred to economists in the past. But making it work called for non-linear random-process theory that did not exist in their day.



Key Sources of Research:


Competing Technologies, Increasing Returns, and Lock-In by Historical Events

W. Brian Arthur

The Economic Journal, Vol. 99, No. 394. (Mar., 1989), pp. 1


Click to access Arthur_1989.pdf


Path Dependence

Scott E. Page

Center for the Study of Complex Systems, University of Michigan, Ann Arbor 48104,


Click to access Page2006.pdf


Increasing Returns and the Two Worlds of Business 

by W. Brian Arthur

April 27, 1996


Click to access Arthur_B.pdf


Path Dependence in Decision-Making Processes: Exploring the Impact of Complexity under Increasing Returns

Jochen Koch,Martin Eisend,  Arne Petermann,


Click to access 0a85e5395b8bf886d7000000.pdf


“Lock-in” vs. “critical masses” – industrial change under network externalities

Ulrich Witt



Complexity and the Economy

W. Brian Arthur

Click to access arthur1999a.pdf


Arrow, Kenneth J.

“Path dependence and competitive equilibrium.”

History Matters. Essays on Economic Growth, Technology, and Demographic Change, Hrsg. Timothy W. Guinnane (2003): 23-35.

Click to access Arrow.pdf



Path dependence, its critics and the quest for ‘historical economics’

Paul A. David

Click to access 0deec53b482217c114000000.pdf


The Complexity Turn

John Urry


Click to access Urry-The-Complexity-Turn.pdf



“Silicon Valley” Locational Clusters: When Do Increasing Returns Imply Monopoly?

W. Brian Arthur




David, Paul A.

“Why are institutions the ‘carriers of history’?: Path dependence and the evolution of conventions, organizations and institutions.”

Structural change and economic dynamics 5.2 (1994): 205-220.

Click to access 00b7d529bc11dee673000000.pdf


Increasing Returns,  Path Dependence and Study of Politics


Click to access Pierson%20APSR%202000.pdf


Increasing Returns and Path Dependence in the Economy

by W. Brian Arthur,

Univ. of Michigan Press,

Ann Arbor, 1994.



Complexity economics:
a different framework for economic thought

W. Brian Arthur



Click to access Comp.Econ.SFI.pdf


Increasing Returns and the New World of Business

by W. Brian Arthur



Click to access HBR.pdf




W. Brian Arthur

Click to access EJ.pdf



Positive Feedbacks in the Economy

W. Brian Arthur

26 November 1989


Click to access SciAm_Article.pdf