Judgmental biases in information markets for innovation evaluationinnovation evaluation

Information markets for innovation evaluation can be prone to biases that may have negative impact on their results.

In researching biases in information markets, it is possible to draw from specic obser-vations of nancial asset markets that are not necessarily related to evaluating in-novations. We highlighted in the previous chapter that information markets are closely related to nancial or asset markets, which underlines the presence of market-related biases in information markets. While specic research on the impact of biases in in-formation markets is still scarce (Wu et al. 2008), we can draw on the large body of experimental and eld research in nancial markets.

One early study tested the extent to which markets can eectively aggregate informa-tion (Plott and Sunder 1982). Informainforma-tion was usually provided in an unambiguous and well-dened form, e.g. as precise likelihoods. The researchers could not refute the no-tion that privately distributed informano-tion is eciently reected by market prices in these environments. In reality, however, no market aggregates objective information, but rather, subjective beliefs. Markets are made by agents that act within the limits of bounded rationality (Camerer and Lovallo 1999) and real market environments often provoke biased actions because information hardly allows for unambiguous processing and application.

In the context of innovation evaluation, information market participants may be subject to the very biases that aect individuals in innovation management, as were presented in the previous section. The underlying rationales for initiating and running informa-tion markets for innovainforma-tion evaluainforma-tion reect the motivainforma-tions that were discussed in the previous sections. While the information market resembles a novel mechanism for innovation evaluation, initiators and participants are comparable to any situation, in which innovations need to be evaluated. We can therefore integrate ndings from the literature with observations from specic conditions in information markets and discuss the impact of biases that are subject to innovation evaluation tasks rather than market mechanisms.

Representation-related biases inuence asset prices and prediction errors in information markets. In eld experiments at Google and HP, potential prediction outcomes were partitioned into multiple stocks, where each stock represented a certain interval band

of the outcome space (Chen and Plott 2002; Cowgill et al. 2008). Yet, another series of laboratory experiments showed that (1) partitions have signicant eect on nal mar-ket prices and that (2) partitions similarly aect experienced traders or marmar-ket experts (Sonnemann 2008). Initiators' partition decisions may negatively impact the ability of participants to reveal valid information. Unpacking one event (of three) into two compo-nent intervals increases its judged probability by about .25 (Sonnemann 2008). Closely related to the representativeness bias discussed in Section 4.3.2, subjects base their in-formation market actions on the amount and concrete boundaries of events that underlie the stocks. Sonnemann (2008) suggests that initiators may reduce potential biases and improve prediction quality by running simultaneous markets that feature dierent par-titions and aggregate results from these markets.

Furthermore, participants in (experimental) asset markets have been frequently found to confuse trading to maximize portfolio values with trading to increase the price of pre-ferred stocks. Subjects do not cease from buying prepre-ferred stocks, even if they receive strong signals that these stocks will likely perform weakly. Research commonly refers to this phenomenon as the wishful-thinking eect. Forsythe et al. (1999) used experimen-tal markets to explore the wishful-thinking eect in political information markets. If subjects have external incentives that motivate high stock prices (e.g. a high vote share of the preferred political party), their trading will be biased towards market-external preferences. Seybert and Bloomeld (2009) ran a set of experiments to discriminate between wishful thinking (trading) based on observing market signals that manifest into larger bets on preferred outcomes, and overestimation that is based on overly positive private beliefs. Their results indicate that market-based wishful thinking will frequently be contagious, causing the private overestimations of other participants. As subjects usually learn about each others' beliefs via bets (or market trades), they start to think wishfully. In the experiments, market trading appeared to positively accentuate the wishful beliefs of a few traders via the market-based wishful thinking of many.

Such an observation can be particularly important in the context of information markets for innovation evaluation. In this case, participants will likely relate the positive out-come of certain stocks with potentially larger personal benet. For instance, potential customers may gain more utility from one potential product characteristic than another, and employees may benet more if information markets predict positive outcomes for objects that were developed in their own departments. In these examples, stocks would include a price premium based on overestimation and wishful-thinking.

Ultimately, overcondence must be considered in the context of information markets for innovation evaluation since certain potential participants are particularly likely to be overcondent and because it has often been observed that overcondent subjects can negatively inuence the outcome of the related asset market.

Section 4.3.2 has shown that overcondence appears more prominent among business founders, entrepreneurs, innovation managers, inventors and those top managers who are more strongly engaged in developing and introducing innovations. Additionally, the previous sections highlighted that nancial markets may particularly attract overcon-dent market agents and suer from their participation. The last decade has provided vivid examples of how a few apparently overcondent individuals can bring enormous losses to very large institutions (Clark 2008). As such, researchers have demonstrated genuine interest in evaluating the impact of overcondence on asset markets (e.g. Mal-mendier et al. (2011)).

Two streams of research in the context of asset markets have investigated the potential impact of overcondent traders on individual gains and market price eciency. One line of research extended classical market models with agents who sacrice rationality for overcondence regarding the precision of their information. In these models, overcon-dent investors overestimate the value of private estimations, hold risky portfolios, trade excessively, and deter stock market prices from reaching their fundamental value (Odean 1998; Daniel et al. 1998; Gervais 2001). Additionally, empirical results have revealed that overcondence signicantly impacts asset trading. Most prominently, overcondent sub-jects show increased trading activity and lowest individual performance in current nd-ings (see Wu et al. (2008) for a review). Closely related to information markets, Deaves et al. (2009) studied the impact of dierent manifestations of overcondence on trading behavior in an experimental asset market. The authors found that overestimation and better-than-average eects are positively correlated and signicantly increment overall trading activity in an experimental asset market. Yet their experiments do not provide insight on how overcondence impacts overall market eciency. Wu et al. (2008) stress that more research relating individual overcondence to market outcomes is needed.

4.5. Summary

This aims of this chapter were two-fold: rst, to provide a more thorough understanding about the origins and impacts of important judgmental biases in the context of inno-vation and its evaluation; and second, to discuss the impact of judgmental biases on

information markets in the context of innovation evaluation.

With regard to the rst target, we found that rich evidence exists demonstrating a detrimental impact of judgmental biases on the success of innovations. With regard to the origin of biases, the results lack clarity. While many articles related the biases in question to motivational or cognitive sources, we can not assert that researchers have attributed the biases to unequivocal origins by mutual consent. It appears that more research is needed to better discriminate (or unite, compare Kunda (1990)) and explain the origins of judgmental biases in the context of innovation evaluation. Based on the literature review, we found ve biases that were most prominently featured. Their char-acteristics and impact in the context of innovation evaluation have been presented in detail above. Out of these, overcondence garnered the most attention by researchers in innovation management. The review shows that overcondence can be considered a driving force behind much innovation activity. Overcondence is frequently found amongst entrepreneurs and managers who particularly embrace innovation. Overcon-dent subjects are more likely see a benet in engaging in innovation and in reaching positive evaluations of innovative undertakings because they underestimate their risk of failure.

Focusing on the impact of judgmental biases on information in the context of innova-tion evaluainnova-tion, the current research in the domain of nancial markets has provided important insights for future research. We found that previous research from analogous domains has explored and analyzed the impact of various judgmental biases on mecha-nisms and situations that can be closely related to information markets for innovation evaluation. The ndings show that important biases in the context of innovation evalua-tion, such as endowment eects, optimism or overcondence, frequently and signicantly impact agents' behavior and outcomes in markets that are closely related to information markets.

In sum, judgmental biases will often have strong inuence on decision-making in the context of innovation evaluation. The literature review reveals that most studies have attributed negative consequences to the presence of judgmental biases. The review of the research has also suggested that information markets may suer from participants' judgmental biases, even though these markets have been deemed highly ecient in ag-gregating information. However, very little empirical insight exists regarding how infor-mation markets function when exposed to judgmental biases in the context of innovation evaluation.

Part III.