4.2.1. Models with “heuristics and biases”

How can we appropriate a new good and compare it to familiar things with the intent to evaluate it? This approach considers the investor to be an actor who develops voluntaristic projects and implements more or less sophisticated heuristics.

In this cognitive vision, the perceptive process plays a central role, because the way in which the investor perceives the interactions with their environment is more important here than objective reality. This perceptive process is itself fed by the experiences that the investor has had and through the game of social connections that they must manage. The meaning given to objects can thus differ from one individual to another, but it is this meaning that will feed these heuristics. Thus, a person who has had a negative experience with a particular location will feel mistrust toward publicity that showcases this kind of environment, whereas a person who has not experienced this may be inclined to have a favorable attitude to this encounter. Someone who considers new products to be a social valorization factor will be less frugal regarding their prices, whereas someone who is not sensitive to this dimension will prefer to wait for the good to become commonplace and the acquisition cost to reduce considerably. One of the first theories to become involved in this logic was Bauer’s theory of perceived risk [BAU 60], which even today remains a strong method of explanation. In his mind, the investor anticipates a certain number of inconveniences that arise from these choices. This investor process will therefore involve developing a strategy to maintain a less risky alternative.

As for locations, two large types of effects explain establishment localization decisions made by an investor. The White effect reduces a major risk; the Todaro effect overestimates hope for gain. The synthesis attempted by Kahnemann and Tversky concerning a double deformation of probability distributions with a psychological reduction of the probability of large loss and an increase in the possibility of a large gain therefore seems pertinent [KAH 16]. Optimism or excessive trust in the White effect reduces a risk connected to location. This White effect contributes a diagnosis of the growth of vulnerability on a planetary level. White is a geographer who saw an increase in the number of houses set behind new dykes, even though the likelihood of the dyke breaking increased the risk of destruction. In international investment, it is estimated that approximately one in four decisions on location quickly proves to be questionable due to underestimated difficulties. The other bias is the Todaro effect, an overestimation of the probability of gain. The Todaro effect is used to explain the growth of slums in urban areas by rural workers attracted by the size of urban salaries, but who have overestimated their chance of obtaining the desired job. The behaviors of entrepreneurs and employment researchers focus on urban areas and create more significant urban areas than if this perceptive error concerning the chance of a company’s success or access to a job did not exist.

For competitive clusters, these two effects are important. The largest innovative enterprise areas are located in vulnerable zones (Silicon Valley for the United States, Nice Sophia Antipolis for France). Their agglomeration is larger than the one arising from simple rational behavior and it creates congestion effects and a sharp increase in housing prices.

4.2.2. Models with preference formation

The most commonly used procedure when choosing the location for an investment depends on a two-phase series. The first stage consists of establishing a short list of “acceptable” candidate locations. The second stage, the final selection, consists of leaving it up to feeling and intuitive judgment. Recent literature lends weight to intuitive judgment and the positive role of emotions in conducting human relations [DAM 03].

This sequence is a learning process; the members of the selection committee generally have not formed preferences on the different location candidates for the establishment. We are therefore in a preference-forming process, and behavioral economics informs us that once the decision is made, the attractive side of the chosen site will be reinforced.

In this kind of selection procedure, there is an intuitive drawing on the complementarity of rational conscious judgment and emotional judgment. For personnel exposed to great danger (soldiers, firefighters, etc.), their training involves having good control over their emotions: mastering them, but also not losing their intuition concerning danger.

Kahneman’s works have proven the double cognitive nature used in decision-making process, rational conscious judgment processes, and intuitive and emotional judgment processes [KAH 16]. In an approach like Kahneman’s, the rational and the intuitive are in a position of substituting one another. Is it not easy to envisage procedures playing on complementarity, calling on both rational judgment and intuitive judgment? This is the question that underlies the analysis of these procedures observed in making investment decisions. Works by Damasio [DAM 03] and Loewenstein [LOE 07] deal with various aspects of these questions without a clear response truly being found for the previous question.

Damasio’s work is interested in comparisons with people presenting brain lesions that deprive them of all emotions. These people drive an automobile better on black ice: they will not use their breaks at the wrong time and will traverse the extremely icy stretch with no problem. These same people will be lost in situations involving games: winning games depends positively on an ability to feel and make decisions with immediate emotions during the playing process. An investment situation seems to be on the negative side of emotions: people accidentally deprived of emotions have shown themselves to be better at making investment decisions in a laboratory context. The results obtained differ depending on whether the situation presented arises from ambiguity or uncertainty. In situations marked by ambiguity, the help of emotions is proven by experiments to have a positive impact; in situations marked by uncertainty, it is negative and a “coldly” rational procedure is preferable.

4.2.3. Coordination models

Since the earliest societies, three components can be found in the functioning of identity: the fact that people have an individual identifier (Little Tortoise, Red Cloud, etc.), a totemic link (from the Thunderbird clan) and a shamanic portion (the wizard can transform into a bear and draw power from this transformation of the individual at each stage of his life). The totemic part is the spatial part; the shamanic part belongs to the changes brought by time. Contemporary anthropology is led to define identity regimes [DES 06]; totems explain a link between Society and Cosmos, unlike predatory animism where there are only human and non-human societies. Totems serve to welcome, commemorate a respectable figure, form an alliance, mock a rival, etc. using conventional codes. One theory of these conventions explains the formation of identity ideals, and a theory arises to complement the theories of self-image.

Economy of knowledge arguments may complete these theoretical approaches. The identifier is a warning side that will lead to the goal by avoiding the cost of learning through trial and error. The attractiveness of a region will depend on the clarity of these signs and these totems.

4.2.4. Argument and limits

We can deviate from the conclusions from a study by Anna Colovic [COL 07]. The approach is limited to one competitive cluster and questions what geographic economics calls the “first circle”. The classification obtained from these factors is as follows:

• – existence of centers of excellence;
• – quality of the scientific workforce;
• – presence of other research teams in the vicinity;
• – presence of recognized universities and research teams;
• – costs for the company;
• – attractiveness of the market;
• – need to adapt to the local market;
• – cooperation between universities and companies.

For a “first circle” location, Anna Colovic’s study insists on the search for highly qualified personnel, the attractiveness created by renowned researchers. In this case, the “behavioral” dimension therefore does not seem to be a very important determiner for this kind of location concerning very rare resources and great skills.

Statistically speaking, the decisions to establish productive activity arise more from strategic decisions concerning location of the product’s life cycle, where organizational competition is the most decisive. In the internal procedures of these companies, a short list of possible settlements is used. Flying geese paradigm localization behaviors are common. This justifies the introduction of behavioral economics considerations for second-circle appeal, particularly for emerging countries.

Companies with intense R&D are still primarily located in the North. They are not very nomadic. The structuration of spaces between rather northern and decisional regions versus more southern regions with a stronger focus on execution tasks is not a fair question; it is a question of structuration. Does this heliotropism have a behavioral explanation? The studies available insist upon the importance of this effect, with structurations being internal to regions, convergence clubs, from the southern side and from the northern side.

Kotler, Haider and Rein’s work [KOT 93] is the foundation of territorial marketing processes. It presents planned and rational action for commercial promotion applied to territories. The critique made today of Kotler’s process arises from the absence of consideration for emotional factors. Emotional marketing is strongly promoted today.

This critique once again indicates the pertinence of the general schema (cognitive, conative, emotion, context) of a behavioral theory of action to create territorial marketing: the “knowledge” process of behavioral economics determines, for example, the relative importance of the emotional factors of attractiveness and this determination indicates the possible scope of an action using this emotional decision. The “knowledge” process of behavior economics indicates the range of the operational process of territorial marketing.

4.3.1. Models with heuristics and biases

In comparative studies on venture capital around the world, two psychological archetypes have been put forward:

• – the first type is the venture capitalist who suffers from exaggerated trust, will underestimate their failures, focus on their success and overreact to the market’s evolution (excessive optimism when things are going well, excessive pessimism when they are not);
• – the second type is that of inadequate personal engagement, and with behavior that is appropriate to their relationship with banking credit. The opportunities offered by new technologies are abandoned to put goods and services on the market through confirmed companies. Their financial engagement is very moderate, whereas the amount of radical innovation is nearly zero. Their attitudes are those of someone waiting behind a desk, whereas econometric studies indicate that rational behavior is that of wide-range prospection of spatial investigation.

For the convenience of this report, we use the term “hothead” to refer to the first psychological type and “penpusher” to refer to the second psychological type. Hotheads are those who act or invest by pointlessly exposing themselves to danger. Penpushers are those who have a bureaucratic idea of their role. Behavioral economics models with heuristics and biases are limited to static use of these psychological types. Dynamic considerations play a role in other methods of modeling, through the formation of preferences or coordination outside the market.

The “hothead” type corresponds to a behavioral profile of entrepreneurial culture, whose use is very significant outside behavioral economics through the psychological profits introduced by Schumpeter and taken into consideration when analyzing the evolutionary economics of innovation. This “optimistic” bias may arise from an overestimation of their expert ability to spread innovation. Seventy-five percent of failures are attributed to a lack of marketing projections. The venture capitalist, who may be a true professional in the difficult evaluation of technological evolutions, may be disproven by the mediocre performances of innovation spread models in terms of projections. Furthermore, optimism concerning the volatility of a commercial result is very common, the estimated ranges of variation (the sales will be somewhere in the range from X to X + dX) are only one-third of those observed, according to the results found by Kahneman and Tversky [KAH 16]. Venture capitalists and entrepreneurs share the same cognitive tasks, and the discussion over heuristics and biases on the part of producers and entrepreneurs leads us to notice the same anomalies in investors since they share a task managing the innovative company.

The “penpusher” psychological type is put forth to explain the poor performances of venture capital, in Canada for example. The official report from 2008 on the situation of venture capital in Canada provides the following explanations: “In terms of investment, American venture capital investors generally adopt more daring investment strategies than their peers in Canada, particularly in the knowledge sector. One suggested reason for this is the particularism of Canadian legislation concerning the nature of the investor’s authorization within venture capital enterprises, legislation on SRCTs (worker venture capital companies). A second reason proposed is the following: the background of the management personnel at financial innovation companies may also be an incidence on the attitudes and the behavior of venture capital investors who are on the market. In the United States, large financial innovation companies are managed by the former managers of industrial companies, particularly high-tech enterprises. A number of these managers are analysts who worked for a given industry before joining the investment banking industry. These managers have vast experience from the start, with generally specialized experience concerning the industries that the financial innovation company they are working for invest in. They are therefore in a position to evaluate the risk associated with the expected investments in these industries more quickly and with greater accuracy. As a result, these managers tend to be surer of themselves and more daring in their investment strategies. In Canada, on the other hand, the managers of financial innovation companies come from the banking industry most of the time and therefore tend to evaluate risk from a banking perspective rather than from the perspective of the company in question. A likely result of this, lacking the deep experience in the industrial industry that their peers in the United States possess, is that most financial innovation companies in Canada adopt a more timid strategy when faced with risk. This factor is particularly important in the case of the knowledge industry, where specialized knowledge is essential for senior executives of financial innovation companies in Canada to be able to elaborate more daring investment strategies” [GOU 08].

The estimation of the yield, Ř_o, depends on two projections, one concerning the potential of an innovation, the other the management ability of a project leader.

where Ř_o is the estimated value of the capital, Θ_o is the projection of the technological innovation’s potential and Ξ_o is the projection of the management abilities of the innovative company.

It has been observed that the most difficult dossiers to evaluate, those that combine both creating a company and technological innovation, are quite often systematically discarded in favor of dossiers that are ironically called “no-risk venture capital”, that is, one offering the simple development of a technology tried and tested by an experienced company. An official report from the late 1980s in France stated “investors prefer to stick to certain data. This leads them to adopt two guidelines: on the one hand, preferring the quality of management over R&D, and on the other hand, favoring projects to develop existing products as opposed to projects to create companies and products”. Kahneman’s model is based on the existing duality in every single cognitive process, one based on conscientious investigation and the other based on taking shortcuts when thinking. Lazy heuristics can be implemented if the difficult element to evaluate is innovation. Let us deviate from simple accounting elements since regulation does not particularly impose innovation content upon us nor the search for the difficulty and discomfort of uncertain evaluations. The “penpusher” type therefore corresponds to these shortcuts in intellectual responsibility introduced by the cognitive process models as explained by Kahneman.

Two sorts of anomalies can correspond to two psychological types: the overreaction of the markets for the first psychological type and a national market with very limited venture capital with very limited engagements for the second psychological type. Schematically, these are the situations relative to the United States and Canada.

4.3.2. Preference formation models

On the technological front, the laws of economy are transforming: innovation is a must. Preferences must be formed and their evolutionary speed is most likely a possible interpretation of the reception of radical innovation. Empirical studies show us the “ideal” profile of the venture capital company and the innovative companies that it finances in part. The venture capitalist is experienced, 14 years of experience says a study by Sheperd and Zacharakis [SHE 04]. They come from a technological milieu that they know well: they slowly assimilate the particular characteristics of their environment. This experience will be challenged by the movement of the technological front, which will introduce new sources of opportunities in horizons that can be far removed from the technological industries in which the venture capitalist has done their best work. The entrepreneur, too, is experienced; they have already performed several product and service launches. Two successful operations are enough to attest to this experience, which multiplies the chances of success for the planned operation by a significant factor. These learning times are not necessarily present in other financial activities, where econometric results, on the contrary, indicate the inexistence of a noviciate period.

Preference formation models depend, in particular, on the types of learning that transform the preference structure. The two forms of learning that are mentioned are learning through experience in an environment of competiveness and vicarious learning through a watchful tutor. Paul Gompers’ analysis of Venture Capital Cycles [GOM 04] mentions the antagonistic cases of bureaucratized companies that underinvest in profitable activities, on the one hand, and in good spin-off opportunities through tutor companies where employees financed by the venture capitalist learn to become managers by accumulating experience, on the other hand. “It is unreasonable to consider that the offer of entrepreneurial talents preexists”, conclude Bernard Guilhon and Sandra Montschaud [GUI 08]. The ability to evaluate technology on the venture capitalist’s part and the managerial abilities of the venture capitalist–entrepreneur tandem result from voluntary policies that will, for example, create entrepreneurial breeding grounds in the high-tech industries.

4.3.3. Coordination outside the market

The fact that venture capital is not simply a matter of prospection is confirmed by the study of its locations. Some urban agglomerations are very highly concentrated in venture capital companies. Just three cities, San Francisco, Boston and New York, record half of the venture capital companies for the United States, which in turn records half of the world’s total venture capital companies. This very pronounced rank-size law corresponds to a common form of distribution for urban agglomerations. The geographic variables often follow a power law. The most common example is that of the agglomeration size, distributed according to a so-called rank-size law. However, this form for the spatial distribution of venture capital is in no way justified by the elementary competition model. Due to transportation costs, this model would lead to greater dispersion over territories [CHE 09], but the elementary competitiveness model provides the following counterintuitive result: venture capital obtains better results abroad than it does within national borders, near its geographic establishment. This is counterintuitive due to the intense monitoring of the innovative company through the venture capital that is supposed to exist. If the best results are obtained by “pure” prospection, why are venture capital companies so strongly concentrated according to geography and why do they develop a policy of establishing local agencies? A virtuous circle of agglomerations exists, where venture capitalists maintain a symbiosis with a colony of innovative entrepreneurs, the two populations supporting one another for their mutual development. The authors conclude with the legitimacy of an attraction policy using territorial collectives for the first establishment of a venture capital company; the others will follow a “flying geese paradigm” [CHE 09].

International studies indicate the contrast between the countries where the professional identity of venture capitalists is constituted and countries where venture capital is inexistent or plays a very small role. This contrast exists, for example, between the United States and Canada within North America, which takes some weight away from the arguments concerning the development of financial institutions and puts an emphasis on the character of the statutes of venture capital companies in Canada, which are most likely ill-adapted, at least in part.

Identity models have been developed by Akerlof and Kranton [AKE 05]. A dynamic formulation of these models takes place through the use of convention models. For venture capital, the empirical data highlight contrasts where national regulatory contexts seem to be decisive. Dynamic modeling can bring back elements from effort convention models by Akerlof and Kranton. The authors have developed a theory of the efficiency of schoolchildren, proposing different types around which adolescents construct a valorizing image in their eyes. The function of the schoolchild’s utility only differs through the final term I_i of the standard utility function in the human capital theory. The term I_i is an image of oneself, which can take at least two modalities. These modalities emerge through a conventional-style dynamic. The establishment proposes the ideal of a Good Student, which may be more or less removed from the schoolchildren’s ideals. The establishment’s management will also be involved in the schoolchildren’s images of themselves.

Venture capital is an adventurous occupation that has a strong incidence on personal identity. The game based on professional identity differentiates it from the operator on the market whose payment very often must compensate for the low contribution to their self image and the personal satisfaction gained from their professional activity. A quite common diagnosis is that venture capital companies lack management and marketing skills. Venture capital demands that the individual have interpretation knowledge in a particular technological field and operational knowledge in business management. This acculturation between a technological domain and the management know-how is not specific to venture capital, but sets it apart from professionals in the field of market finances and banking intermediation.

The characteristics of a professional identity model differ from those of a social identity model, as seen in an article by Akerlof and Kranton [AKE 05]. The point of reference for Akerlof and Kranton’s model is that of social development psychology. In childhood, the imitation of a behavioral ideal has great importance for individual identity. The start of adolescence remains dependent on behavioral determinations through the desired conformity to collective ideals.

An adventurous profession is based on autonomy and detachment from the determinations of simple social imitation. Expertise often sets heuristics in motion, and these shortcuts in thinking are challenged during the failures of these great professionals. These shortcuts may also be favored by a regulatory and tax framework, turned more towards the regulation of the “hotheads” from finances and innovation.

4.3.4. The contribution of behavioral approaches to the analysis of venture capital

Behavioral finances allow a unified framework to be put forth for the study of the investor’s behavior. It raises new questions, e.g. concerning the convergences of behaviors between investments in companies listed on the stock exchange and those not listed, where the investment cycles, for example, by highlighting the gap between over-reactive behaviors on venture capital and the counter-cyclic behaviors of innovators, which, given the drop in industrial and merchant activity, have more time to file patents and renew ranges of products and services.

The development of new models, like the dynamic models of professional identity, is promising and they feed both academic reflections and professionals’ concerns.

Behavioral finances also play a critical role in the sense that, since Keynes, arguments on the importance of behavior arise from the framework of standard rationality and are very often used. Behavioral finance must be sure to carefully distinguish what arises from social identity, mechanisms of social integration and professional identity. A lack of vigilance and poor evaluation of one’s own ability to analyze and act operationally remain the major causes of failures in financing innovations, and these causes of failure are strongly underlined by the approaches of behavioral financing innovation.