What is the decision support information demand and supply equilibrium?

by Daniel J. Power
Editor, DSSResources.COM

At equilibrium the demand for decision support data and information equals the supply. In this era of low cost "big data", the equilibrium quantity demanded is very large. Economic theory can help understand decisions made by managers within a firm about decision support and analytics. Decision support data and information is a subset of all data and information obtained, stored and created within organizations. Decision support information is intended for use in managerial and organizational decision making. For some managers, the demand for decision support data and information is impossible to satisfy, it is unbounded and limitless, other managers are more realistic and assess the cost of obtaining and producing an information "good". Acquiring and analyzing more data has a cost. Economic concepts like supply and demand and marginal cost and marginal return can help understand the trade-offs that must be contemplated even though internal data acquisition and processing decisions are not market based. These fundamental concepts can serve as analogies or metaphors that improve our analysis.

Technically, equilibrium is a price-quantity pair where the quantity demanded is equal to the quantity supplied. Measuring both price and quantity is challenging for decision support information "goods". Varian (1998) defines an information good as "anything that can be digitized--a book, a movie, a record, a telephone conversation". Decision support information goods have high returns to scale, there is a high fixed cost of production but a low marginal cost of reproduction. Managers make decisions about decision support information needs when investing in data storage like data warehouses and newer capabilities like data lakes. The overall goal should be anticipating decision information needs and economically meeting those needs. The goal should be finding an equilibrium, a balancing of the supply of decision support information goods and demand for such goods. The equilibrium of decision support information is the intersection of the decision support information supply and demand curves.

At equilibrium, consumption of information for decision making is balanced with the supply of decision support information. The marginal cost of more decision support information is equal to the marginal benefit of more decision support information. In traditional microeconomics, the supply and demand equilibrium point provides the most efficient use of resources. Demand for decision support information is a function of "price" or "cost" per unit. Demand comes from both algorithms and from human decision makers. The supply of decision support information has differing value or "price" depending upon relevance, timeliness and novelty/uniqueness. In organizations, the supply of decision support information is large and the quantity demanded is also generally very high. Cost/price per unit and elasticity of demand determine the actual amount of decision support information demanded in a specific decision situation.

Due to technology and behavioral changes, at the level of a firm the cost/price per unit of decision support information has been steadily falling for many specific decision situations. It seems reasonable to ask if there is an equilibrium in each decision situation where the provision and consumption of information is the most efficient? If so, will that point yield a result that is better than that of a satisficing heuristic (Simon, 1956)? or does using satisficing in decision making determine the equilibrium? Also, can managers anticipate and regulate the supply and demand for decision-relevant information?

Herbert Simon argued that many naturally occurring decisions/problems are characterized by computational intractability or a lack of information. This constraint or limitation precludes using mathematical optimization procedures and hence people can and should satisfice and accept that the information needed to find a satisfactory solution is "good enough". There is no equilibrium in these decision situations.

In most organizations, there is an imperfect market for decision support information. Powerful decision makers (buyers) can "afford" more information than the equilibrium amount and the available decision support information from suppliers may be hard to assess or find. Search can be biased as well. The micro-economic supply and demand model may be a weak metaphor for decision information consumption and production, but one would hope that rational decisions are made to generate or produce "new" types of information to "sell". For example, creating business intelligence systems, developing business analytics applications, and commissioning decision support special studies should be informed, rational decisions. Investments in decision support information should be carefully evaluated to assess the potential worth of supplying the "new" information before making expenditures. One should not and with hope would not spend $1 million to conduct a decision support special study with a potential worth of $500,000.

In general, data and information lack value unless used to inform decision-making, but having access to data and information of a particular sort doesn't mean it is useful for informing a specific decision or class of decisions. If demand for decision support information is relatively inelastic (the quantity demanded doesn't change much with price increases or decreases), then investments that increase decision support information supply will decrease the equilibrium price of that information. However, some would argue demand for decision support information is perfectly elastic and that more decision support information is always sought and desirable at a given price so an increase in supply will increase the equilibrium quantity demanded. Spending more to increase use of currently or previously captured data does not however create value unless the expenditures will significantly inform and impact a particular decision. Some might argue that because of self-service analytics the supply of decision support information has or will greatly increase at the same or a lower cost/price. This claim assumes all of the analyses are relevant, accurate, and useful to inform a specific decision.

Yates (1991) conducted a detailed historical analysis that examined investing in information in a manufacturing firm. She examined information in a pre-digital or early digital era. Yates concluded "Acquiring information involves making large and often long-lasting investments in capital, human and nonhuman. The decisions to make these investments are shaped by many organizational and technological factors, as are their consequences. The assumption that firms can buy information in increments of any size and that they will do so at the moment when its value exceeds its theoretical cost is appealing but may not reflect the unfolding dynamics of information acquisition and use within firms." Managers create and purchase information in large chunks, the process is often political, and changes in use is challenging.

A related problem was discussed by Ackoff (1967). He noted "For a manager to know what information he needs he must be aware of each type of decision he should make (as well as does) and he must have an adequate model of each. These conditions are seldom satisfied. Most managers have some conception of at least some of the types of decisions they must make. Their conceptions, however, are likely to be deficient in a very critical way, a way that follows from an important principle of scientific economy: the less we understand a phenomenon, the more variables we require to explain it. Heace, the manager who does not understand the phenomenon he controls plays it "safe" and, with respect to information, wants "everything." The MIS designer, who has even less imderstanding of the relevant phenomenon than the manager, tries to provide even more than everything. He thereby increases what is already an overload of irrelevant information." (p. B-149)

Another concern is that the supply of some types of information available in organizations is heavily subsidized and this subsidy alters the equilibrium (see Figure 1). With a price subsidy for the supply of some information, a greater quantity of that supply of information would be demanded. The lack of relevance or usefulness of that information in a decision situation may not counterbalance the ease of access and relatively lower price of using readily available information. The heavy reliance on easily available data and on currently collected data reflects this subsidy effect. Building Business Intelligence systems and Data Warehouses subsidize the provisioning of certain types of information like dashboards. Because of the subsidy, information producers (suppliers) are encouraged to produce more of certain types of information and they are able to do so quickly and often at a low cost. Sadly, the relevance of the information to a particular decision may be tangential.

Is there a need to balance the supply of information with the demand for information in an organization? As noted, the demand depends somewhat on the price, but the price is poorly understood by managers and information is purchased in large "chunks" and information can actually increase in value as more like information is gathered. The dilemma facing Information Systems professionals and Business Analytics staff is the need to recoup the large fixed cost of investments that are made in hardware, software, specialist staff, and data gathering. For these reasons, sometimes information search seems more a search of convenience and using what we have than a consequence of a rational search plan. Information search in organizations may be likened to this well-worn story:

"A police officer sees a drunken man intently searching the ground near a lamppost and asks him the goal of his quest. The drunk says he is looking for his car keys, and the officer helps, but then he asks whether the man is certain that he dropped the keys near the lamppost. 'No,' is the drunk's reply, 'I lost the keys somewhere across the street.' 'Why look here?' asks the surprised and irritated officer. 'The light is much better here,' the drunk responds with confidence."

For more on this comical allegory that "depicts the biases inherent in many types of scientific research" see Quote Investigator (2013). In general, we want to avoid the observational bias shown by the streetlight effect where people only search for something where it is the easiest or most convenient to look. Managers should avoid the drunkard's search, cf., Freedman, 2010.

Alexis and Wilson (1967) identified "One of the fundamental information problems of large organizations is this: Information is costly, hence organizations must balance the gain from information against the cost of obaining it. Organizations thus seek to develop systems that generate information with many end uses (multi-purpose information systems). But many decision situations require specialized information, useful for that situation alone. This is costly to provide. To improve the quality of information where high information collection costs cannot be justified, managers must find ways to reduce collection costs." (p. 337)

According to Evans and Wurster (1997), information is a large percentage of the cost structure in many firms and information connects and holds together the structure of firms. Their Harvard Business Review article seems especially prescient and insightful in hindsight. As they explained, the new economics of information is impacting many industries. Because of increasing digital disruption, it is time to deconstruct and re-envision organizational decision making structures and better use information for decision making to create a new demand/supply decision information equilibrium in organizations. One hopes the new equilibrium provides large amounts of high quality, real-time, customized decision support information both for algorithms and to a distributed group of interacting decision makers.

The law of diminishing marginal utility does not seem to hold for decision support information. Based upon diminishing marginal utility, one would expect that with increased consumption of decision support information, satisfaction with more units of decision support information should decrease. Demand seems to be high and continually increasing.


Alexis, M. and C. Wilson. Organizational Decision Making. Englewood Cliffs, NJ: Prentice-Hall, Inc., 1967.

Evans, P. and T. S. Wurster, "Strategy and the New Economics of Information," Harvard Business Review, SEPTEMBER–OCTOBER 1997 issue at URL

Freedman, D. H., "Why Scientific Studies Are So Often Wrong: The Streetlight Effect," Discover, JULY-AUGUST 2010 ISSUE, Friday, December 10, 2010 at URL

Quote Investigator, “Did You Lose the Keys Here?” April 11, 2013 at URL

Varian, H. R., "Markets for Information Goods," 1998 at URL

Varian, H. R., "Pricing Information Goods," June 1995 at URL

Yates, "Investing in Information: Supply and Demand Forces in the Use of Information in American Firms, 1850-1920," in Inside the Business Enterprise: Historical Perspectives on the Use of Information, Ed. P. Temin, January 1991 at URL

Figure 1 Supply and Demand for Decision Support Information
The rightward shift in supply results from the adoption and implementation of new technologies. P = Price per unit, Q = Number of units

Last update: 2018-10-28 04:42
Author: Daniel Power

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