laura brandimarte

Laura Brandimarte

Negative Information Looms Longerthan Positive Information

withAlessandro AcquistiJoachim Vosgerau

The research question

• In November 2006 two Ottawa employees of a grocery store chain made admissions of theft on the message board of a Facebook group

• In January 2007 they were fired after their employer found out about the message

• About two years ago two users created a Facebook page for Keep A Child Alive, a nonprofit organization that helps provide drugs for people, living in Africa and India, who are affected by AIDS

• There was no consequence to the creation of this page

• How does information related to past events and retrieved today get discounted? Does information about past events with negative valence receive more weight in impression formation than information with positive valence, as time passes?

• How can we address human biases and make environments secure and trustworthy?



• Trustworthiness of a technological environment depends on:− secure physical infrastructure− enforcement of well-defined regulations and policies− how such physical and regulatory infrastructure is

eventually interpreted and used by individuals

The research questionFrom “The Upside of Irrationality,” by Dan Ariely

When the designers of modern technologies don’t understand our fallibility, they design new and improved systems […] that don’t take our limitations into account (I like the term “human-incompatible technologies,” and they are everywhere). As a consequence, we inevitably end up making mistakes and sometimes fail magnificently.


What the literature focused on:

Slope of the red line is larger inabsolute value at each point in time

We introduce the hypothesis of differential discounting:

Slope of the red line is smaller inabsolute value at each point in time

• Impact of info with negative valence lasts longer than impact of info with positive valence, not only because of asymmetric effects of valence, but also because of different weights – or discount rates – applied to the two types of info

• Note on terminology: we use the term “discount rate” to refer to past events

Hypothesis of differential discounting

• Survey-based randomized experiments, manipulating valence of information provided to subjects and time to which that information referred

• All subjects (CMU students) received the same baseline information about a person or a company– subjects in the neutral conditions only received baseline info– subjects randomized to good or bad info conditions received same info

except for one detail with either positive or negative valence, referring to a more or less recent past

• Then subjects were asked to express a judgment on the person or company they just read about– that judgment, measured through various indices, represents the

dependent variable of interest

Testing our hypothesis: Three experiments

• In order to guarantee robustness we varied several design features across experiments

– DV: measures of inferred vs. direct judgment– object of judgment: people vs. company– different time frames: weeks vs. months vs. years– time manipulation: sometimes emphasized by moving the position of

the relevant piece of information in the text, other times left in the same position of the text


• For each experiment, we estimated a difference-in-difference model described by the following equation:

DVi = β0 + β1Badi + β2MoreRecenti + β3Bad_MoreRecenti + β4Agei + β5Malei + εi

where the Dependent Variables of interest changed across experiments

• This specification allows to disentangle the effects of time, valence and their interaction. Demographics are added for descriptive analysis and randomization checks

• We were mostly interested in the coefficient on the interaction term, β3, which tells us whether time has a differential effect on information with positive and negative valence

• We expected β1 and β2 to be positive, and β3 to be negative


• Rules: subjects are to decide how they would split $1 with another player – the recipient

• Priming subjects on what to consider a fair split: subjects are instructed that on average allocators keep for themselves 70% of the sum

• Experimental manipulations of valence and time– the recipient is described as having played as the allocator in 7 previous

rounds– allocations were all average (~ 70-30 split) except one, which was:

either generous (50-50 split, good info conditions) or unfair (100-0 split, bad info conditions)

and occurred either 6 or 2 rounds ago (old, middle and recent conditions respectively).

• Neutral condition: all allocations were fair

Experiment 1: Allocation game

Good old condition

Recipient’s previous decisions on how to split the sum of $1


To Himself To otherRound 1 72 28

Good old condition



Round 2 50 50


Good old condition



Round 2 50 50

Round 3 69 31


Good old condition



Round 2 50 50

Round 3 69 31

Round 4 70 30

Round 5 71 29

Round 6 68 32

Round 7 70 30


Good recent condition



Round 2 69 31

Round 3 70 30

Round 4 71 29

Round 5 68 32

Round 6 50 50

Round 7 70 30


Bad old condition



Round 2 100 0

Round 3 69 31

Round 4 70 30

Round 5 71 29

Round 6 68 32

Round 7 70 30


Bad recent condition



Round 2 69 31

Round 3 70 30

Round 4 71 29

Round 5 68 32

Round 6 100 0

Round 7 70 30


• Dependent variables:– money allocation between subjects and their opponents– fairness assessment of their opponents

• We expected the slope of the line describing average allocations to be smaller in absolute value for bad info conditions than for good info conditions

• Note: For all experiments, DV is not expressed in levels, but it’s the absolute difference between values in each condition and the average value of the neutral condition


Experiment 1: Allocation game - Results

Average sum that subjects chose to allocate to themselves in the allocation game

• Based on pair-wise t-tests, good information allocations do not differ from the neutral allocation except for the recent condition

• On the other hand, both bad information allocations differ significantly from the neutral allocation

• Bad information is not discounted, good information is

• Intentionality Moderates this effect

Experiment 1: Allocation game - Results

All coefficients are of the expected sign and significant

• Hypothetical scenario: subjects are to express a judgment on a company based on background information we provide

• Neutral condition: only baseline info is provided

• Experimental manipulations of valence and time: we add to the baseline information one detail with either positive or negative valence, and vary the time to which that detail refers

Experiment 3: The Company Study


“Here is some background information about a company called PitStop. Please review this information, and be ready to answer the questions below and in the next page. PitStop was founded in 1961 as a mechanics garage. After a while, it started to manufacture its own brake discs - discs were previously imported from Great Breaker. The production of brake discs was also followed by the manufacturing of other braking system components, which allowed the company to enter the market of racing vehicles. In the meantime, in the aim to develop in the field of motorcycles, PitStop acquired 70% of the company MagMoto, specialized in producing wheels for racing motorcycles. In 1977 [2009] PitStop received the National Entrepreneurship Award for accounting and financial transparency [was heavily fined for accounting and financial fraud].

In order to compete in the sector of brake discs for industrial vehicles, PitStop took over the company SlowDown for car brake disc machining.”

• Dependent variables:– how subjects would have liked to work for the company– and how likely they would be to choose that company as their

main sponsor

• These questions were created in order to infer a measure of liking for the company and a sense of belonging and of agreement with its policies, which can’t be measured by typical indices, like stock prices or profitability indices

• We expected the slope of the line describing average measure of judgment to be smaller in absolute value for bad info conditions than for good info conditions


Figure 7. Average level of liking indices across conditions in Experiment 3

Experiment 3: The Company Study - Results

• Similarly to what we found in previous experiments, the good information indices do not differ from the neutral averages except for the recent condition

• On the other hand, bad information average indices differ significantly from the neutral averages

• The data partially support our hypothesis: while for the second index all our predictions are met, coefficient β3 is small and positive for the first index – but not at all significant


• With the results of three survey-based randomized experiments, we provide empirical evidence that bad is discounted less than good, and thus the effects of bad last longer than the effects of good, not because the immediate effect of bad is larger, but because a smaller discount rate is applied to it as compared to the one used for good

Conclusions

• Important implications of differential discounting in the era of Web 2.0 applications− On the one hand, there is a drive to make oneself popular− On the other hand, information with positive (or neutral) and

negative valence are processed in two very different ways− OSNs may be perceived as secure environments: people reveal

lots of personal information, but estimate consequences and risks incorrectly

Conclusions

Thank you!

Questions…

Extras

Agenda• The research question

– Motivation– Our contribution to the literature

• Hypothesis: differential discounting of bad and good information

• Testing our hypothesis: Three experiments– Experimental design– Empirical analysis and results– Discussion– Limitations

• Conclusions

• How does information related to past events and retrieved today get discounted? Does information about past events with negative valence receive more weight in impression formation than information with positive valence, as time passes? How does information with positive and negative valence affect perceived security and trust?

• The literature (Baumeister et al., 2001; David et al., 1997; Brickman et al., 1978) looked at the longer lasting effects of bad relative to good, but not at the possibility that two different discount factors may be applied to them


The research questionFrom CharlotteObserver.com

If you say something on social networks that puts your employer in a negative light, "that's not very different than an employee standing on a corner and holding a sign or screaming it. It's public, and it's out there for the world to see. Individuals can forget that it is a very public forum."That's because many people get lulled into a false sense of security, thinking only their friends can see or read what they're posting. They forget, however, that others might also be able to see it, depending on their privacy settings, and there's always the chance a friend might copy the material and pass it to others outside the circle.

What the literature focused on:


We introduce the hypothesis of differential discounting:

• Impact of info with negative valence lasts longer than impact of info with positive valence, not only because of asymmetric effects of valence, but also because of different weights – or discount rates – applied to the two types of info

• This may be due to– mobilization effects (Taylor, 1991) and evolutionary theory (Baumeister

et al., 2001)– negativity bias (Seligman and Maier, 1967)– negative info is more attention grabbing (Pratto and John, 1991)

• Note on terminology: we use the term “discount rate” to refer to past events

Hypothesis of differential discounting

• Several challenges in experimental design– separating the effects of good and bad: the need for baseline

information

– preventing confounding effects: of magnitude and/or rarity on judgment of valence on memory of age on impression formation

– defining an index of judgment for a company: we are not interested in a financial assessment of the company, for which we could have used stock market data, but a measure of sense of belonging and agreement with its policies


Experiment 1: Dictator game - Results

Experiment 1: Dictator game - Results

Figure 4a. Average sum that subjects chose to allocate to themselves in the dictator game - modified

Experiment 2: The Wallet Story - Results

• Hypothetical scenario: subjects are to express a judgment on a person based on background information we provide

• Neutral condition: only baseline info is provided

• Experimental manipulations of valence and time: we add to the baseline info one detail with either positive or negative valence, and vary the time to which that detail refers

Experiment 2: The Wallet Story

“Here is some background information about Mr. A. Please review this information, and be ready to answer the questions below and in the next page. Mr. A was born in San Diego, California, where he attended elementary and middle school. As a child, he obtained his social security number and received the standard DPT vaccination. When he was 16 years old, he moved to Sacramento, California, with his family. He attended high school there and got his driving license. Just about graduation, he found a lost woman's purse containing $10,000 in cash. He reported [did not report] the discovery to the police, and the rightful owner retrieved [did not retrieve] her money. After graduation he moved to Houston, Texas where he has been living and working for the past 12 months [5 years].”


• Dependent variables:– how much subjects liked the person described; how they would

have liked to work with her (Interpersonal Judgment Scale, Byrne 1961)

– and whether they considered her trustworthy (General Social Survey, World Values Survey)

• We expected the slope of the line describing average measure of judgment to be smaller in absolute value for bad info conditions than for good info conditions


Figure 6. Average level of liking and trust indices across conditions in Experiment 2



• Based on pair-wise t-tests, values of liking indices in the good-old info conditions do not differ from the values in the neutral condition

• The trust index is the only one for which the good-old info condition differs from the neutral one

• On the other hand, all bad information average indices differ significantly from the neutral averages


• The data strongly support our hypothesis, as all coefficients have the predicted sign and are statistically significant.

Figure 6a. Average level of liking and trust indicesacross conditions in Experiment 2 - modified


Figure 7a. Average level of liking indices across conditions in Experiment 3 - modified


• Why should differential discounting occur? We can think of three possible explanations– asymmetric mobilization effect generated by negative events: the

human organism responds more intensely to bad events than to good or neutral events at every level – physiological, emotional, behavioral, judgmental (Taylor, 1991). This mechanism itself may be reconnected to evolutionary theory (Baumeister et al., 2001)

– negativity bias (Seligman and Maier, 1967) and overestimation of the probability of a person behaving unfairly given that she behaved unfairly once in the past, relative to the probability of a person behaving fairly given that she did so once in the past. The weight given to one negative episode will be larger than the weight given to the corresponding positive episode

– info with negative valence is more attention-grabbing (Pratto and John, 1991)

Discussion

• The results of our experiments provide some evidence in support of differential discounting

• Asymmetry effects can’t be the only explanation because the slope of the line describing judgment in bad info conditions is generally smaller than it is for good info conditions

• Our results cannot be explained by effects of valence on memory (Kreitler and Kreitler, 1968; Skowronski and Carlston, 1987; Bless et al., 1992; Matling and Strang, 1978)

• We intentionally separate the effects of good and bad, so we do something different from what the criminology literature on redemption does (Blumstein and Nakamura, 2009)

Discussion

• All our experiments provided hypothetical scenarios (Bhatia and Fox-Rushby, 2009). In order to make our results more convincing we plan to repeat Experiment 1 in the lab, so that subjects face real allocation decisions

• Population was not heterogeneous

• Limitations of survey-based experiments and reliability of our metrics for Experiment 3

Limitations

laura brandimarte

Documents

valence of information

positive valence

baseline information

negative valence

relevant piece of information

asymmetric effects of

time6impact of info

types of info