Objetivos Específicos

The information overload hypothesis predicts that if the total amount of information about a choice set grows too large, a negative impact on decision making will result (Jacoby, Speller, & Kohn, 1974; Jacoby, Speller,

& Kohn-Berning, 1974). Decision-makers confronted with more information make less informed choices (Lee

& Lee, 2004; Lurie, 2004), and Greifeneder et al. (2010) find choice satisfaction decreases as choice set size increases when options described on increasing number of attributes.

Information overload occurs as cognitive limits prevent the processing of relevant information to the appropriate degree. The human cognitive system suffers from issues of ‘computational constraint’: we are limited cognitive processors due to limitations of attention and memory, with neither motivation nor ability to make the largely intractable calculations dictated by rational choice theory (RCT; Anderson, 1983; Gilovich

& Griffin, 2010; Hastie & Dawes, 2001; Simon, 1979). The standard RCT approach views humans as

“omniscient calculators”, who can perform the expected complex computation tasks to reach a decision (e.g.

calculating the subjective expected utility of all possible outcomes) given adequate motivation and information.

This view of homo economicus has been critiqued from a variety of viewpoints, not least because people have a poor ability understanding problems involving probability (Tversky & Kahneman, 1973; 1983; for a review, Kahneman, 2011).

Gigerenzer and Goldstein (1996) point out that people do not need to calculate their optimal behaviour functions, they need only use ‘successful’ algorithms to approximate the most optimal behaviour; and term these ‘fast & frugal’ cognitive algorithms ‘heuristics’. (for a review, see Gigerenzer & Gaissmaier, 2011).

Gigerenzer & Gaissmaier (2011) define a heuristic as “a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods” (p.454).

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Heuristics make judgments faster and less effortful through frugality, by: (i) examining fewer cues, (ii) reducing the effort of retrieving cue values, (iii) simplifying the weighting of cues, (iv) integrating less information, and (v) examining fewer alternatives (Shah & Oppenheimer, 2008). Kahneman and Frederick (2002) add to this, proposing that a heuristic assesses a target attribute by another property (attribute substitution) that is more easily accessible.

Information-processing approaches to examining voter decision-making have become popular in recent years (Lau & Redlawsk, 2001, 2006), examining the selection of an alternative (or ‘candidate’) from a finite number of alternatives contained in a choice set, based on its attributes (e.g. policies, candidate personal information, campaign details). Heuristics may help voters choose between varying numbers of alternatives, all of which may have differing number of attributes. For example, by: editing or eliminating unfamiliar candidates from the choice set (familiarity heuristic), or choosing that option that is most familiar (recognition heuristic; Gigerenzer & Gaissmeier, 2011); or choosing the alternative best on a key attribute (‘Take-The-Best’); or not computing weights for each attribute (i.e. use a simple -1/0/+1 score ‘tallying’ method). The alternative being calculating overall values (utilities), for each alternative, weighted by their perceived importance, and again by the probability of an outcome occurring given the choice (i.e. a calculation of risk given uncertainty).

Lau and Redlawsk (2001; 2006) provide some of the most prominent heuristics voters seem to utilize in vote-tasks (i.e. candidate party affiliation, candidate ideology, endorsements, polls, & candidate appearance).

Of all of these, candidate appearance has been studied the most, with prominent work by Todorov and Uleman (2002; 2003) on trait judgments of likeability, competence, trustworthiness, and aggressiveness, from single glances or 100ms exposure to a face (Willis & Todorov, 2006; Todorov et al., 2008). Trait judgments of competency predict the winning candidate in a variety of real-life and lab-based elections in the US with only 100ms exposure; 66-72% of time in the 2000, 2002, 2004 US Senate elections (Todorov et al., 2005), 68.5%

of the time for state-governor elections from 1996-2006 (Ballew & Todorov, 2007); and in 2006, 72.4% of the Senate elections and 68.6% of the gubernatorial elections (Ballew & Todorov, 2007).

The other ‘political heuristics’ have received less experimental attention, with other authors positing that candidate partisan identity (e.g. being a Labour or Conservative politician) acts as a heuristic in voter

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tasks (e.g. Rahn, 1993). Partisan identities are stereotypic schemata with which voters can infer a large amount about those with a partisan label (e.g. ‘liberals are for high taxes’, ‘Republicans are anti-government’, ‘Tea-Partiers are crazy’). Acceptance and application of these partisan stereotypes result in a schemata-based affective response, though this is mediated by how consistent with the stereotype the target seems to be (Collange, Fiske, & Sanitioso, 2009). Gaissmaier & Marewski (2011) investigated party label as a recognition heuristic, with results from four major German elections showing that mere recognition of party names forecast parties’ electoral success fairly well.

Political heuristics may be used in isolation as appropriate, or operate in tandem to produce some form of compound decision. In keeping with general views on heuristics, Lau and Redlawsk (2006) find that heuristic use depends on a person’s overall cognitive style, and are increasingly utilized in low-information environments, or where the DM has limited time or cognitive capacity; and that heuristic use varies as a function of political sophistication. Lau and Redlawsk (2006) found that heuristic use among sophisticates improved correct voting, while it decreased correct voting among non-sophisticates, however, this effect reverses when sophisticates were presented with non-stereotypic candidates. This seems intuitive- heuristics only approximate optional decisions if they are correctly applied given the informational environment.

In prior studies by Lau and Redlawsk, partisan cues needed to be learned in bi-party elections of 2-6 candidates through intentional access of informational items; yet this is not reflective of reality, where partisan cues (e.g. party brands) are ubiquitous and salient. In electoral contexts outside of the US, which are predominantly multi-party electoral contexts, heuristics (such as partisanship) should narrow information search down to a few alternatives, and voters may engage in a more effortful comparative depth of search within them. Decreasing correct voting rates may reflect the fact that voters never get the opportunity to learn about potentially more congruent candidates, as they are biased from the outset. If heuristics bias our decision-strategies in such a way as to decrease correct voting, then we should be able to observe bias where partisanship is a global cue rather than a learned one. Results in Lau and Redlawsk (2006) seem to support such a rationale, and as such we explore this in Chapters 2 and 3.

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