Entropy in economics: its measurement through primary data and its usefulness in business

Author:Elias Sanidas
Position:School of Economics, University of Wollongong

Since Georgescu-Roegen's ("The entropy law and the economic process", 1971) pioneering work on entropy in economics, not many scholars have preoccupied themselves with this concept and its consequences. Recently some revival is taking place to link entropy and the business world (see for example "The entropy vector, connecting science and business" by Handscombe and Patterson, 2004). ... (see full summary)


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Since Georgescu-Roegen's (1966, 1971) pioneering work on entropy in economics1, several scholars have preoccupied themselves with this concept and its consequences. Recently some revival is taking place to link entropy and the business world; see for example Handscombe and Patterson' s (2004) paper as a general and simple exposition of the issue; Khalil (2004) who made an overall critique of Georgescu-Roegen's work; Smith and Foley (2008) who rigorously demonstrated the links between entropy and economic general equilibrium theory; Ayres (1994) who related entropy to many aspects of information, economics, evolution, and progress. Briefly, entropy is the natural universal process according to which there is a tendency for wasting energy so that increasing disorder and decreasing efficient productive work takes place in a system unless some actions occur that slow down this process. Handscombe and Patterson (2004, p. 1) define entropy very simply as "the degree of disorder or chaos that exists or is created". Thus, "entropy in a closed system must remain constant or increase" (Moore, 2007, p. 38).

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From Handscombe and Patterson, (2004), we can isolate some variables to represent entropy in business (see Table 1). Note that these variables although included in one of the three categories (columns in Table 1) can also be used in the other categories but in the opposite direction. Thus an entropy-increasing variable can also be an entropy-decreasing variable in a different context. A good example is leadership which can generate decisions that are either entropy-decreasing or the contrary. It would be useful to quote these authors for some of these variables.

Business plans provide a way of trying to think ahead in order to organize (i.e. minimize) disorder in the way that a business is developed (ibid, p. 134).

A company's ability to change is determined by its vision multiplied by its leadership and resources, all divided by the corporate age (ibid, p. 20); the corporate age is the inertia to do with culture, attitude, procedures, practices (ibid, p. 20).

Information must be considered as a negative term in the entropy of a system...We have only partial information and entropy measures the lack of information (ibid, p. 28).

The entropy vector encourages us to realize that the lack of setting broad tolerances is as important as setting the specific objectives (ibid, p. 44.

Experience curve, which is simply the continuous resetting of the entropy vector (ibid, p. 46).

A lack of collaboration can be equated with entropy (ibid, p. 85).

And so on.

The aim of this paper is to check whether these factors (and others) as seen in Table 1 are important indeed in a real case. We will use as a real case a sample of 80 firms2 from the marine industry in Australia (the survey was conducted during 2006 and 2007). For this purpose, we need a theoretical model which will help us to properly analyse the above real case in order to detect any signs of entropy. Recently Sanidas (2005, 2006) has introduced a comprehensive model of describing and explaining a firm’s dynamic path in the economy. This model includes -in a succinct way- all major factors that shape the firm’s evolution in

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growth. The theory of the firm is extended to encompass all types of opportunity costs and not just transaction costs. As a further extension to the transaction costs and capabilities development and as a synthesis of several related issues, Sanidas (2006) has introduced a complete system of 4 mutually exclusive, interdependent and negentropic3 processes (PROBB) that fully describe the contents of the black box4 of production (see Table 2). This model as Table 2 shows is a comprehensive summary of all elements that uniquely describe and explain the way firms are organized, managed and grow. These four PROBB are interdependent, although each PROBB contains unique elements that cannot belong to another PROBB.

The important point to notice in this Table 2 with the PROBB variables is that each one of them represents opportunity costs. Thus, the POC variables represent transaction (or friction) costs, the POS elements stand for strategic opportunity costs, the POW variables express wisdom costs and the POM factors generate kinetic costs. All these different types of opportunity costs contribute to the natural law of entropy. However, it must be reminded that the PROBB variables can be both entropy-increasing and entropy-decelerating depending on how they are used in a business context. This will also be the object of investigation in the present paper.

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The remaining of the paper will be as follows. Section 2 will introduce methodology and present the results; and section 3 will discuss conclusions.

Methodology and results

The model PROBB contains about 150 variables out of which about 120 are directly related to the elements of Table 2 above, and the remaining are more performance or economics related. The former will be called the X, Y, Z, and W PROBB variables

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corresponding to the four processes respectively (POS, POW, POM, and POC); whereas the latter will be called the V variables. They are:

V1: Customers dictate terms and requirements (the opposite of the 7-score Likert scale would be: we, the firm, dictate these terms)

V2: Suppliers dictate terms and requirements (as for V1)

V3: Rivals are weak (strong):

V4: Power we, the firm, have in dictating prices and/or quantities

V5: Low cost strategy (low, high)

V6: Low price strategy (low, high)

V7: Niche market strategy

V8: Product quality

V9: Product uniqueness

V10: Availability of large market for our product

V11: Adoption of technical innovations

V12: Creation of technical innovations

V14: Technology choice affected company operations (low, high)

V15: Company operations affected technology choice

V21: Sales growth in the last 5 years

These V variables represent the consequences of the importance and direction of all PROBB variables. Thus we would expect that some PROBB variables-for a given firm- are rather negative in their action towards the Vs. Those PROBB variables that are not well related to Vs -for a given firm- can be termed the potentially entropy increasing and those PROBB variables that are well related to Vs can be termed the...

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