Fundamentals of bias in decision making

Human biases can be described as skewed subjective perceptions of objective realities.

Biased subjects are deterred from accessing, memorizing or utilizing objectively diagnos-tic information, but instead yield alternated versions of reality after mental processing.

Biases result from invalid external information cues or invalid cue processing. In the latter case, subjects transform valid external information cues into invalid internal re-ections of these cues. Actions are considered biased if subjects apply invalid information to form expectations, make evaluations, or render decisions that would be considered incorrect based on an objective assessment of publicly available information. There has been an ongoing debate on the origins of biased decision making by human agents. The predominant view in behavioral economics attributes biased decisions to systematic cog-nitive processes that underlie human information retrieval and processing (Nisbett and Ross 1980). In social psychology, however, a considerable strand of research has argued for considering motivational factors or aective states as important drivers of biased decision making (Kunda 1990). Some researchers have long called for an increasing inte-gration of cognitively and motivationally-induced biases (Tetlock and Levi 1982), while others add that motivational and cognitive biases have distinct origins and character-istics, and that more benet comes from analyzing and understanding both concepts attributively and interactively (Moore and Healy 2008; Hilbert 2012). In the following sections, we will introduce both approaches to explain the origins of biases, and highlight their specic formation and characteristics.

4.2.1. Biases from heuristics and cognitive processing

Biases from heuristics and cognitive processing result from skewed information retrieval or processing. The popular heuristics and biases terminology was coined by Daniel Kahneman and Amos Tversky, who studied phenomena that indicated ineective hu-man information use, leading them to develop theoretical approaches to help under-stand the underlying mechanisms (Goldstein and Hogarth 1997). Cognitive biases occur when subjects systematically access and process distorted information via heuristics from memories or environmental cues (Kahneman 2003). Several such systematic distortions have been introduced to the current body of research. In the following paragraphs, a few cognitive biases will be introduced. We will also highlight the notion that heuristic decision making is not necessarily inecient or biased.

First, humans frequently retrieve skewed information that overly contributes to the for-mation of specic beliefs. Subjects tend to equate the presence of inforfor-mation to the relevance of that information. This will frequently, for example, lead subjects to overesti-mate the relative probabilities of certain events that have more exposure than others due to media coverage and which lack valid data. Anchoring eects document the systematic misuse of information. Subjects make evaluations based on present cues or anchors, even if these cues are objectively unrelated to the focal evaluation task or imply implausible results (Mussweiler and Englich 2005).

For example, subjects overestimate the proportion of fatalities related to accidents com-pared to more subtle illnesses because accidents are more frequently visualized and sensationalized by the media (Serfas 2011). Similarly and more closely related to inno-vation evaluation, successful new products are more often featured in the press than ops, which may induce an overestimation of success probability for new products in general. Furthermore, anchoring may prevent innovators from discriminating between potentially successful innovations. An innovator may perceive the degree of an innova-tion's novelty as a positive per se because it may underline the personal creative eort.

However, the innovator would likely underestimate the potential negative eects of nov-elty, e.g. having to explain the innovation or train potential customers.

Of course, heuristics do not always lead people to bad or awed decisions.

Many researchers have found empirical evidence that simple heuristics can, in fact, be very eective in human decision making (Gigerenzer and Todd 2008). For instance, Scheibehenne and Bröder (2007) show that lay people can provide similarly good pre-dictions for the outcome of tennis tournaments compared to ocial experts by merely recognizing player names. In another eld experiment, forecasts based on name

recog-nition were not only as accurate as statements about voting intentions in predicting federal and state elections in Germany but even worked well with very lousy samples (Gaissmaier and Marewski 2011). Such ndings underline the assertions of prominent critics like Gerd Gigerenzer, who suggest not to focus the study of heuristics on awed results (Gigerenzer and Brighton 2009); a focus on the biased use of heuristics does very little to explain the ecological validity of heuristics in general (Gilovich and Grin 2002).

In fact, [...] equating limits with failure, and lack of limits with success, may underlie a deep misunderstanding about the consequences of omniscience, which may inhibit the retrieval of really relevant information] (Gigerenzer and Todd 2008).

In sum, a large body of research has explicitly studied heuristics that produce cogni-tive biases, while other researchers argue that such a focus systematically misrepresents human decision-making heuristics by focusing on the below-average distribution tail of heuristic decision making (Gigerenzer and Brighton 2009). We agree with the proponents of studying cognitive biases, who point out that certain biases arise systematically by awed heuristics (Kahneman 2003) and that awed heuristics imply distortions in information acquisition and processing. It is by no means uncommon in the business world or science to focus a study on distortions in order to ultimately overcome them (Serfas 2011).

4.2.2. Biases from motivation

The previous section highlighted that subjects often make ill-informed decisions, even if they were properly motivated. In this section, we will focus on motivation. The following paragraphs will provide a brief introduction to the concept of motivation and show that motivation may preemptively interfere with the rendering of valid decisions because it forms uncorrelated subjective goals (Kunda 1990).

Motivation is commonly referred to as subjective goal formation. Motives are related to emotions; they reect preference or susceptibility for specic classes of incentives of similar background. Such preference nds expression in analogous disposition to per-ceive and evaluate situations. The dispositional character of motives allows observation of motivations only if they are stimulated by motive-relevant situations. A motive man-ifests in a subject's tendency to observe and engage in situations in a specic manner (Rothermund and Eder 2011).

Motives are considered relatively stable over time. They are inherited or learned. Fig-ure 4.1 presents the basic model of traditional motivation theory. Motivations are

con-Figure 4.1.: Classical model of motivation from motivational psychology (Source: Rhein-berg (1997))

sidered structures that are embedded in a person and which are triggered by relevant stimuli in a given situation. Such stimuli can be located inside or outside the subject.

Intrinsic motivation draws from internal stimuli by which an action itself provides the focal subject with pleasure or satisfaction. Extrinsic motivation draws from external stimuli that drive action via material or non-material rewards. Material rewards can relate to the acquisition of nancial benets whereas non-material rewards can relate to aspects of personal security or external appreciation (Heckhausen and Heckhausen 2006). Only the interaction of subject, stimuli and situation create motivation. Conse-quently, the motivation controls or inuences behavior, i.e. perception, evaluation and action (Rheinberg 1997).

Biases from motivation in (innovation) evaluation occur when individuals pursue goals that are independent of objectively accurate decisions or evaluations. In this case, intrinsic and extrinsic stimuli provide incentives that bring the subject away from mak-ing objectively valid evaluations.

Pyszczynski and Greenberg (1987) developed a model that describes how intrinsic stim-uli for self-enhancement can inuence motivation to bias decision making. According to their model, the goal of maintaining high levels of self-esteem and the human ten-dency towards biased hypotheses and testing creates biases that lead to the creation of overly positive self-images. Biased hypothesis testing refers to the assumption that decisions are generally formed based on developing a hypothesis and then collecting in-formation to test it. However, humans will tend to search for inin-formation that supports the hypothesis because they are more likely to retrieve case-positive information and put more value on information the earlier it is retrieved Pyszczynski and Greenberg

(1987). For instance, subjects who were told they performed badly in an intelligence test attributed more validity to critical reports than supportive ones for the importance of applying intelligence tests during job interviews (Pyszczynski et al. 1985).

Shepperd et al. (2008) highlight that intrinsic stimuli often motivate self-enhancement, which may lead to biased evaluations. Subjects make self-serving attributions because they benet in self-worth. Hence, they assume responsibility for the desired outcomes but neglect responsibility for outcomes that are not desired. In the context of innova-tion evaluainnova-tion, such self-serving eects may prevent participants from validly assessing an innovation's value. Innovations that benet other participants more than the focal subject are thus more likely to receive bad reviews by the subject, even if the task urges objective evaluation.

In sum, motivations are essential drivers of human behavior. Biases from motivation occur because subjects are exposed to stimuli and situations that drive motivation, and behavior that hinders the rendering of objectively valid evaluations. Alicke and Sedikides (2009) found very strong empirical evidence supporting the important role of motivation in imposing goals that prevent subjects from making objectively valid evaluations.

4.3. Systematic literature review of research on