Heuristics and biases have been linked to entrepreneurship by many authors (see Zhang and Cueto, 2017); often, however, the concepts of biases and heuristics used are either muddled or are not effectively delimited. This is particularly the case as heuristics is as much about ignoring as acknowledging the information that can impact on entrepreneurial commercial success: what information to ignore and what to attempt to influence. Furthermore, as the world is endlessly complex, even the potentially perfect heuristic ordering of cognitive ability cannot avoid the impact of unknowns. To model this, a hypothetical commercialization possibility frontier is proposed: a success that is the 100% attainable with exactly the right
22 In the neoclassical sense of perfect competition markets. The demand-market logic does
include the Hayekian concept of competition as a never-ending process of uncertain and ex-ante unknowable outcomes; “Competition is a procedure of discovery, a procedure involved in all
evolution, that led man unwittingly to respond to novel situations; and through further
competition, not through agreement, we gradually increase our efficiency” (Hayek, 1992: 19).
“Competition is thus, like experimentation in science, first and foremost a discovery procedure.
… Competition as a discovery procedure must rely on the self-interest of the producers, that is it must allow them to use their knowledge for their purposes, because nobody else possesses the information on which they must base their decision” (Hayek, 1998: 68, 70).
23 Marketing literature includes a tradition concerning the creation of demand, such as by means
of advertising, by marketing departments and through marketing activities. While demand can likely be awakened, meaning that the customer base grows, by firm activities, ultimately, in terms of the reading of the judgement-based approach to entrepreneurship used in this paper, demand arises from the subjective valuation and free choice of customers (Foss & Klein, 2012). A similar formal argument for this can be found in Stigler & Becker (1977).
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combination of resources, the right choices at the right time, and not negatively impacted by the sum of all other market participants choice sets. In such a case, the agent heuristically acted on the right information that should have been acted on, ignored the right information that should have been ignored, and no unknown information impacted on the commercialization. The last part of this aligns with Knight (1921), Alchian (1950), and Smith (2003) as learning over time improves a firm’s output if the unknown or ignored factors or their impact do not change fundamentally.
As uncertainty arises from a lack of perfect foresight into the actions of other market participants and the compounded nature of the interaction of these, “the
producer, then, must estimate (1) the future demand which he is striving to satisfy and (2) the future results of his operations in attempting to satisfy that demand”
(Knight 1921: 237). The heuristic issue is that while uncertainty about current and future information is philosophically objective, it can only be experienced subjectively. This process is mapped in Figure 1. In this paper, it is argued that information points can be further modeled into three types: unknowns
, actionable , and ignored
. Each quantum of information involves a likelihood and an impact magnitude on the commercialization-possibility frontier. For instance; the outbreak of a local war in most countries is likely low; however, its potential impact is considerable. It is important to note that, for the validity of the model, it does not matter whether the contexts are ignored deliberately or a result of biases—the result is the same: the entrepreneurial agent mentally discounts their impact or likelihood to zero. The same is true of unknowns, which pertain to much of the world (Hayek, 1945, 2002). The actionable group is however information that the entrepreneur believes it would be valuable and possible to attempt to
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impact or change. The composition of these types represents the heuristic of the entrepreneurial judgment informing entrepreneurial belief.
Figure 1—Heuristic model of entrepreneurial judgment
The paper hence proposes viewing each group of information as a sum, which combine to represent the objective world now and in the future (as the future is unknowable, the farther ahead in time the entrepreneur is guessing, the larger becomes). It is furthermore important that while the members of the actionable grouping are selected by means of the heuristic, this is not in any way a guarantee that the action can be performed or that the result will be desirable or planned, as is the case in the illustration above with the hiring of a salesperson. The model is moreover rational, in the sense that it requires the ignoring of certain information because of the cost of obtaining it (Stigler, 1961). The heuristic, it is argued, informs the Belief and, as time progresses, is potentially
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updated (the “Potential Experience Input” box in Figure 1) as a sorting method for information points, which is similar to the BAR framework’s more loosely defined treatment and selection effects (Foss & Klein, 2018). This makes the model one of ecological rationality based on trial and error (Smith, 2003). It also aligns the model with cognitive approaches’ ideas of temporality impacting on entrepreneurship and agents changing the way they think and perceive as time passes (Churchill & Bygrave, 1989; Moore, 1986; Hindle, 2004). The model also mirrors Alchian (1950), who suggests that while the profit motive is the generally accepted motivation for firms, and it may look as though firms are maximizing profit over time, this is merely the result of market choices and evolution observed ex-post; hence the model can explain a multitude of entrepreneurial motivations (Wry & York, 2017). In summary, the model proposes an explanation of the mental process involved in pursuing opportunities for rational entrepreneurial beliefs, even when they seem irrational to outsiders, such as entering highly complex and uncertain industries.