Paper ISEAD DO maximizers predict better than satisficers

8/7/2019 Paper ISEAD DO maximizers predict better than satisficers

1/35Electronic copy available at: http://ssrn.com/abstract=1754927Electronic copy available at: http://ssrn.com/abstract=1754927

Do Maximizers PredictBetter than Satisficers?

_______________

Kriti JAIN

J. Neil BEARDENAllan FILIPOWICZ2011/18/DS/OB



Do Maximizers Predict Better than Satisficers?

Kriti Jain *

J. Neil Bearden**

Allan Filipowicz***

This paper can be downloaded without charge from the Social Science Research Network electroniclibrary at: http://ssrn.com/abstract=1754927

* PhD Candidate in Decision Sciences at INSEAD 1, Ayer Rajah Avenue, Singapore 138676,Singapore. Email: [email protected] Corresponding author.

** Assistant Professor of Decision Sciences at INSEAD 1, Ayer Rajah Avenue, Singapore138676, Singapore. Email: [email protected]

*** Assistant Professor of Organisational Behaviour at INSEAD 1, Ayer Rajah Avenue,Singapore 138676, Singapore. Email: [email protected]

A Working Paper is the authors intellectual property. It is intended as a means to promote research tointerested readers. Its content should not be copied or hosted on any server without written permissionfrom [email protected] Click here to access the INSEAD Working Paper collection
http://www.insead.edu/facultyresearch/research/search_papers.cfmhttp://www.insead.edu/facultyresearch/research/search_papers.cfm



Abstract

We examined the relationship between maximizing (i.e. seeking the best)versus satisficing (i.e.seeking the good enough) tendencies and forecasting

ability in a real-world prediction task: forecasting the outcomes of the 2010FIFA World Cup. In Studies 1 and 2, participants gave probabilistic forecastsfor the outcomes of the tournament, and also completed a measure ofmaximizing tendencies. We found that although maximizers expectedthemselves to outperform others much more than satisficers, they actuallyforecasted more poorly. Hence, on net, they were more overconfident. Thedifferences in forecasting abilities seem to be driven by the maximizerstendency to give more variable probability estimates. In Study 3, participantsplayed a betting task where they could select between safe and uncertaingambles linked to World Cup outcomes. Again, maximizers did more poorlyand earned less, because of a higher variance in their responses. Thisresearch contributes to the growing literature on maximizing tendencies byexpanding the range of objective outcomes over which maximizing has aninfluence, and further showing that there may be substantial upside to being asatisficer.

KEYWORDS: Maximizing; Satisficing; Forecasting; Predictions;Overconfidence


4/35

Maximizing vs. Satisficing and Accuracy

3

Do Maximizers Predict Better than Satisficers?

Simon (1955, 1993) proposed satisficing as a descriptive alternative to the normative

maximizing objective that guides the behavior of neo-classical agents. According to him, actual

people are more apt to search for something that is good enough (i.e., that satisfies by sufficing)

than they are to try to find the thing that is the absolute best (i.e., that maximizes). More

recently, Schwartz and colleagues argued that individuals varied stably in their tendency to

maximize vs. satisfice (Schwartz, Ward, Monterosso, Lyubomirsky, White, & Lehman, 2002),

with more recent literature examining how this individual difference leads to differences in

objective outcomes (Bruine de Bruin, Parker, & Fischhoff, 2007; Iyengar, Wells, & Schwartz,

2006; Polman, 2010). Following Simons original emphasis of satisficing involving choice

behavior, this empirical work on individual differences in satisficing tendencies has focused on

choice and decision behavior. In the current paper, we examine whether satisficing (versus

maximizing) tendencies are associated with judgment quality. In particular, we test whether

satisficers or maximizers do better in a forecasting task with true exogenous uncertainty:

predicting the outcomes of the 2010 FIFA World Cup.

Where one stands on the satisficing - maximizing continuum has an impact on decision

making outcomes. Maximizers report making more poor decisions based on a self-report

decision outcome inventory (Bruine de Bruin et al., 2007; Parker et al., 2007). Polman (2010)

found that maximizers simply seek and chose more alternatives, ending up with both better and

worse decisions than satisficers. The maximizers in Polman's study reported making worse

decisions, as assessed by Bruine de Bruin's (negative) Decision Outcome Inventory, but they also

reported making better decisions on a measure containing items relating to positive decisions.

And in an impressive demonstration of the objective benefits of maximizing, Iyengar et al.


5/35


4

(2006) found that graduating students who scored higher on Schwartz et al.s (2002) maximizing

measure secured higher paying jobs than did lower scoring students.

While data is accumulating that maximizing tendencies influence decision making,

several researchers have asked about the breadth of behavior influenced by maximizing.

Schwartz noted that "it remains to be determined whether maximizers also consistently act

differently from satisficers" (2002, p. 1195), while Iyengar wondered whether maximizing

tendencies were global individual difference measures or "simply a set of learned behaviors or

search strategies designed specifically for decision-making tasks, and not necessarily even all

decision-making tasks" (2006, p. 148). We explore the breadth of impact of this individual

difference by looking at an important antecedent of decision making, probabilistic forecasting, as

well as a related judgment task, estimating one's relative performance on those probabilistic

forecasts.

Accurate probabilistic forecasting, the ability to correctly assign high probabilities to

events that will occur, and low probabilities to events that will not, is essential to effective

decision making. Consider the most proximal example, sports betting, an activity with millions

of participants (e.g. in the UK alone, the turnover from betting on the FIFA 2010 World Cup was

estimated to have been between 1 and 3 billion pounds). Obviously, successfully assessing odds

is crucial to good (long-term) performance. In a health context, a physician with highly

inaccurate assessments of outcome likelihoods would have difficulty recommending alternative

courses of treatment. And in an organizational context, correctly assessing the probability that

interest rates (or the stock market, or currency exchange rates, or even product demand) will

move in a certain direction, would allow one to adequately take advantage of and also protect

against such movement.


6/35


5

In contrast to the positive relationship between maximizing and decision making

outcomes (Iyengar et al., 2006; Polman, 2010), two preliminary findings hint that maximizers

might be worse at making accurate probabilistic forecasts. Bruine de Bruin and colleagues

(2007) argued that maximizers have worse decision making processes overall, based on

maximizers' lower scores on a seven-scale behavioral decision making competence inventory.

Most relevant to probabilistic forecasting, maximizers did worse on the consistency of risk

perceptions sub-scale. In this subscale, maximizers were more likely to assign higher

probabilities to events happening in the next year than to events happening in the next five years

(i.e. happening in the next year plus four other years), and assigning higher probabilities to

events in a subset (e.g. dying in a terrorist attack) than to events in the corresponding superset

(e.g. dying from any cause).

Second, and related to the lack of consistency of risk perceptions finding, maximizers

tend to show higher variance in responding (Polman, 2010). Polman showed that maximizers

alternated more between decks in the Iowa Gambling Task (Bechara et al., 1994), and that this

inflated response variability drove down their earnings, as their switching increased their

sampling from the bad deck. In making probabilistic forecasts, higher response variance that

is driven by superior discriminating skills, e.g. by a forecaster who can discriminate precisely

between teams that win their matches and those that do not, will not affect the accuracy of the

forecasts. All other sources of variance, for example variability driven by needlessly

maximizing, as Polman observed, would lead to worse forecasting accuracy.

In the studies described below, we examine whether maximizing tendencies are related to

the objective outcome of probabilistic forecasting accuracy, and test the effect of variance of

responding as a possible mechanism linking maximizing tendencies to forecasting accuracy.


7/35


6

And since variance of responding driven by superior discriminating skills should not affect the

accuracy, we look both at overall response variability, and at the unnecessary variability left after

factoring out the variability that can arise from superior discriminating skills.

Given that we can measure how well participants did on the forecasting task, we are also

able to examine a related and well-documented judgment bias, peoples tendency to overestimate

their relative performance. In one classic demonstration, Svenson (1981) asked drivers to

estimate their driving abilities (relative to others in the experiment), and found that 93% of a US

sample and 69% of a Swedish sample put themselves in the top 50% of drivers. This pattern of

self-enhancing judgment has been observed across a range of tasks with a wide variety of

samples (e.g., Zuckerman and Jost, 2001), and has been dubbed the better-than-average effect

(Alicke et al., 1995).

Yet it remains an open empirical question whether maximizers would overestimate their

relative performance. Bruine de Bruin and colleagues' (2007) behavioral measure of decision

making competence included an underconfidence/overconfidence scale, which assessed

participants' abilities to recognize the extent of their knowledge. Maximizers were less aware of

the extent of their knowledge, but the scale does not allow one to adjudicate between

underconfidence (respondents do better than they thought) and overconfidence (respondents do

worse than they thought). Maximizers self-report less life satisfaction, optimism and self-esteem

and report more regret and depression (Schwartz et al., 2002), which suggests that they may bias

their self evaluations downward. However, since maximizers have highest standards for

themselves and prefer not to settle for second-best (see sample items from the maximizations

scale by Diab, Gillespie, and Highhouse, 2008, which we present below), it is also reasonable to

expect that they might bias their self evaluations upwards. But overconfidence is a combination


8/35


7

of self-evaluation (how one thinks one did) and objective measures (how one actually did),

making the net effect indeterminate. With no a priori hypotheses, we carried out a pilot study to

test the relationship between maximizing and the better-than-average effect, before studying

overconfidence in the context of probabilistic forecasting.

We report below on this pilot study and three subsequent experiments. The pilot study

looked at the relationship between maximizing tendencies and the better-than-average effect.

Study One tested whether maximizing tendencies were associated with actual forecasting

performance, and the extent to which this relationship was driven by differences in response

variability. Given the results of the pilot study, we also examined the relationship between

maximizing tendencies and overconfidence. Study Two replicated Study One, but with a time

lag on the forecasting data, by using a second wave of predictions gathered 2 weeks later. In

Study Three we then examined whether maximizing tendencies were linked to outcomes in a

decision making (betting) task that implicitly required judgmental forecasts, and again tested the

mediating effect of response variability. Apart from the pilot study, all studies were conducted

just prior to or during the 2010 FIFA World Cup, and all of the forecasts and decisions were

linked to World Cup outcomes. Using tasks linked to real-world events with truly uncertain

outcomes allows us to better assess the degree to which maximizing might be linked to judgment

in uncertain economic settings compared to general knowledge type tasks that involve only

epistemic uncertainty.

PILOT STUDY

To examine whether a relationship exists between maximizing tendencies and

overconfidence, we included a better-than-average type question and a measure of maximizing


9/35


8

tendency as a filler task in another, unrelated study. Two-hundred subjects completed the

Maximizing Tendency Scale (Diab, Gillespie, and Highhouse, 2008), and also estimated the

percentage of participants completing the survey who would have driving skills inferior to their

own. In line with the results reported by Svenson (1981), 75% of the respondents placed

themselves above the median (50%) in driving ability. More importantly, their judgments of

relative driving skill were positively related to their score on the maximizing scale (r = 0.21, p

Documents

Paper ISEAD DO maximizers predict better than satisficers