A Psychological Approach to How Trust is Built and Lost in the Context of Risk

J. Richard EiserUniversity of Sheffield, UK

Mathew WhiteFriedrich-Schiller Universität, Jena, Germany

Structure of this talk How risk depends on human decisions. Decisions and their consequences. Trust as a social judgement about decision-makers

and information sources. ‘Marginal trust’ – changes in trust as a consequence

of specific events. Contributory factors – negativity bias, cognitive

consistency, diagnosticity, decision types Conclusions.

Risk depends on human decisionsRisk involves uncertainty about the

likelihood of events and the value of their consequences

Risk arises from interactions between people and their social and physical environment.

Risk depends not only on physical conditions but also on human actions and decisions (e.g. Chernobyl, Hurricane Katrina, Kashmir earthquake).

Risks are socialPoor decisions exacerbate risk for ourselves

and others. We often rely on others to manage and

alleviate risks on our behalf.We often rely on others to inform us about

risks and advise us what to do.Inequality within and between societies

increases vulnerability and limits access to help and information.

Hence…Understanding risk involves understanding

not only physical conditions but also how people make decisions.

Risk perception implies judgements about the quality of our own and others’ decisions.

Experts should make higher quality decisions and/or give higher quality information (or else they’re not experts).

What do we mean by ‘quality’?Within the context of risk management:

Ability to discriminate danger and safety.Use of appropriate criterion for balancing

different costs and benefits.Within the context of risk communication:

These, plus…Avoidance of bias due to personal interest.Use of appropriate criterion for warning about

danger (neither too alarmist nor complacent).

Decisions and their consequences

In an uncertain environment, we need to differentiate between safety and danger.

Some situations are clearly safe, others are clearly dangerous.

What happens in between?An approach derived from the psychology

of perception: Signal Detection Theory.

Discriminating danger

Danger

Safety

Risky criterion

Cautiouscriterion

?

Decision-outcome combinations

When deciding whether something is safe or dangerous, there are four possibilities:

Dangerous – treat as dangerous (“Hit”).Dangerous – treat as safe (“Miss”).Safe – treat as dangerous (“False alarm”).Safe – treat as safe (“Correct all clear”).

ConsequencesThese different combinations can have

different costs and benefits.Misses can often appear more costly than

false alarms. An excessively precautionary approach can

deprive users of benefits of a technology, and/or expose them to alternative, perhaps greater, risks (e.g. using cars after a train crash).

Trust as a social judgement

Trust in experts implies a positive judgement of the quality of their decisions and/or information.

Trust can depend on implicit estimates of the others’ competence, partiality and honesty.

If ‘experts’ are seen as having a vested interest, this may undermine trust.

Decision-makers who share one’s interests and values are more trusted.

Example 1: Mobile Phones

Respondents rated different sources of information about possible health risks of mobile phones in terms of:Trust.Knowledge.Warning criterion (how much evidence source

would need before warning).Industry seen as knowledgeable, but

reluctant to warn and therefore distrusted.

Knowledge

0 1 2 3 4 5

Tru

st

0

1

2

3

4

5

Environmentalists

Media

Medics

Government

Scientists

Industry

Warning criterion

0 1 2 3 4 5 6

Tru

st

0

1

2

3

4

5

Environmentalists

Media

Medics

Government

Scientists

Industry

Example 2: Contaminated land.

Local residents rated different sources of information about possible health risks of contaminated land in terms of:Trust.Expertise at judging how safe or dangerous.Bias in decision-making/communication.Openness.Having residents’ own interests at heart.

Perceived expertise does not guarantee trust without impartiality, openness and shared values.

How much would you trust what each of the following might tell you about risks from

contaminated land?

If there was contaminated land in your neighbourhood, how able do you think each of the following would be to judge how safe or dangerous

it was?

Conclusions of surveys

Baseline levels of trust only partly reflect perceived expertise.

Perceived self-interest, openness and shared values are also important.

Need for an experimental approach to unconfound these factors.

Need to examine how specific events may influence marginal trust.

Marginal trustMany policy makers know public trust is low

But how can they build it & avoid losing it?

Four psychological insights:

1) Negativity bias (prior)

2) Desire for cognitive consistency

3) Information diagnosticity

4) Decision outcome types (Miss, False Alarms etc.)

1) Negativity bias

"Bad is stronger than good" (Baumeister et al, 2001; Rozin & Royzman, 2001)

Info. valence Effect on trust

Positive Small increase Negative Large decrease

Trust = easier to lose than gain (trust asymmetry)

“Trust comes on foot and leaves on horseback”

Slovic (1993)

Valence Trust

Negative -4.73

Positive +3.07

F(1,102) = 82.64, p<0.001 pη2 = .45.

Terrible News !!!!!! -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Local board authority can close plantResponsive to any sign of problemsLocal advisory board establishedEmployees carefully trainedEmployees rewarded for finding problemsOn-site government inspectorEmployees informed of problemsCommunity has access to recordsNeighbours notified of problemsPublic encouraged to tour plantTry to meet with publicEmployees closely supervisedEPA monitor radioactive emissionsNo problems for five yearsConduct emergency trainingRecord keeping is goodManagers live near plantHold regular public hearingsMandatory drug testingNearby health better than averageEvacuation plan existsEffective emergency action takenNo problems in past yearNo evidence of withholding informationOperates according to regulationsContribute to local charities

Don't contribute to local charitiesSerious accident is controlledOfficials live far awayLittle communication with communityAccident occurs in another stateNo public hearingsEmergency response plans not rehearsedPublic tours not permittedAccused of releasing radiationDelayed safety inspectionsDenied access to recordsPoor record keepingHealth nearby worse than averageEmployees not informed of problemsOfficial lied to the governmentPlant covered up problemNo adequate emergency response planEmployees drunk on jobRecords were falsified

e.g. Keep good records

e.g. Keep bad records

Increase

trust

Decrease

trust

45 "events" in a nuclear power plant

2) Desire for cognitive consistency

People want stability in their belief structures

We tend to trust good news about things/from people we like but not for things/people we don’t (Hovland, Janis & Kelley, 1953)

People don’t like nuclear power

So greater effect of bad news may be due to a confirmatory bias

What about a less negatively viewed industry?

Negativity or cognitive consistency?

Sample = 68 students

1) Attitudes (-3 to +3): Nuc. = -.47; Phar. = +.50, p < 0.01

2) DV Trust change (Slovic,1993; Cvetkovich et al. 2002)

“How would your level of trust in the management of a particular nuclear power (pharmaceutical) plant be affected by the following information?”

(‘Much less trust–3 to Much more trust +3)

Negativity or cognitive consistency?

• 12 events (6 positive & 6 negative) either nuclear power or pharmaceuticals

0

1

2

3

Nuclear Pharmaceuticals

Industry

Abs

olut

e ef

fect

on

trus

t

Negative

Positive

F(1,66) = 8.16 , p < 0.01, pη

2 = .11

Negativity or cognitive consistency?

So ‘Trust Asymmetry’ isn’t ubiquitous

Replicated in other domains (e.g. additives)

Good news for already trusted sources but doesn’t help distrusted sources build trust

Fortunately there is more to the story

Slovic (1993)

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Local board authority can close plantResponsive to any sign of problemsLocal advisory board establishedEmployees carefully trainedEmployees rewarded for finding problemsOn-site government inspectorEmployees informed of problemsCommunity has access to recordsNeighbours notified of problemsPublic encouraged to tour plantTry to meet with publicEmployees closely supervisedEPA monitor radioactive emissionsNo problems for five yearsConduct emergency trainingRecord keeping is goodManagers live near plantHold regular public hearingsMandatory drug testingNearby health better than averageEvacuation plan existsEffective emergency action takenNo problems in past yearNo evidence of withholding informationOperates according to regulationsContribute to local charities

Don't contribute to local charitiesSerious accident is controlledOfficials live far awayLittle communication with communityAccident occurs in another stateNo public hearingsEmergency response plans not rehearsedPublic tours not permittedAccused of releasing radiationDelayed safety inspectionsDenied access to recordsPoor record keepingHealth nearby worse than averageEmployees not informed of problemsOfficial lied to the governmentPlant covered up problemNo adequate emergency response planEmployees drunk on jobRecords were falsified

e.g. Keep good records

e.g. Keep bad records

Increase

trust

Decrease

trust

Look at the variance!

Some good news is very good for trust

Some bad news is not so bad for trust

Unpacking why might help us build trust

3) Information diagnosticity

We make the world simpler by categorising others E.g. Friendly/Unfriendly; Honest/Dishonest etc.

The info. we use varies in terms of diagnosticity i.e. how good is it at differentiating people

One important aspect = information specificity i.e. relate to a single event or many events

Jo took £10 from the till …. a) last Wednesday or b) every day last week

3) Information diagnosticity

Slovic info. differed in terms of specificity:

A) High specificity: Events “A plant official is found to have lied about a safety matter.”

B) Low specificity: Policies “There is careful selection and training of plant employees.”

Trust should be more affected by policy (low specificity) than event (high specificity) info.

Re-analysed data in terms of events vs policies

Re-analysis of Slovic (1993)

High specificity info.

(EVENTS)

Low specificity info.

(POLICIES)

-8 -6 -4 -2 0 2 4 6 8

Change in trust-8 -6 -4 -2 0 2 4 6 8

Change in trust

Negative policies

Positive policies

Negative events

Positive events

0

1

2

3

Event Policy Event Policy

Abs

olut

e ch

ange

in tr

ust

Negative

Positive

Re-analysis of Slovic (1993) + new study

Reanalysis New StudyValence: F(1, 102) = 82.64, p < 0.001 F(1,35) = 7.61, p < 0.01Specificity: F(1, 102) = 3.89 , p = 0.051 F(1,35) = 12.19, p < 0.001V X S: F(1, 102) = 118.17, p < 0.001 F(1,35) = 13.26, p < 0.001

0

1

2

3

Event Policy Event Policy

Abs

olut

e ch

ange

in tr

ust

Negative

Positive

Re-analysis of Slovic (1993) + new study

Reanalysis New StudyValence: F(1, 102) = 82.64, p < 0.001 F(1,35) = 7.61, p < 0.01Specificity: F(1, 102) = 3.89 , p = 0.051 F(1,35) = 12.19, p < 0.001V X S: F(1, 102) = 118.17, p < 0.001 F(1,35) = 13.26, p < 0.001

3) Information diagnosticity

Trust asymmetry exists for events (high specificity) but not for policies (low specificity)

a) Bad events have large negative effects on trust

b) Good events have small positive effect

c) Good and bad policies have similar large effects

Conclusion: Promote positive policies not events!

4) Event types Our final psychological insight again suggests it‘s a

little more complicated

4) Event types Our final psychological insight again suggests it‘s a

little more complicated

Thought reactor operations were

“Dangerous” “Safe”

Reactor Dangerous A) HIT B) MISS

really was Safe C) FALSE ALARM D) ALL CLEAR

Which engineer would you trust/distrust most?

4) Event types Our final psychological insight again suggests it‘s a

little more complicated

Thought reactor operations were

“Dangerous” “Safe”

Reactor Dangerous A) HIT B) MISS

really was Safe C) FALSE ALARM D) ALL CLEAR

Which engineer would you trust/distrust most?

Risk communication

What about if you learned that some of them had tried to cover up their mistakes?

PredictionsH1) Discrimination ability: Correct > Incorrect

Hits & All Clears > False Alarms & Misses

H2) Response bias: Caution > Risk

Hits & False Alarms > All Clears & Misses

Benefits of Hit loom larger; Costs of Miss loom larger

H3) Communication bias: Transparency > Reticence

Open > Closed

-3

-2

-1

0

1

2

3

Dangerous Safe Dangerous Safe

Tru

st c

hang

e

Correct

Incorrect

Open Closed

Predictions

FA

FA

H

AC

M

M

AC

H

Communication bias

4) Event types 189 Students with three different scenarios :

1) Nuclear power - tank corrosion

2) Vaccine - holiday

3) Computer virus - in uni library

Between Ps design per scenario:

2 (discrimination ability - correct/incorrect) x

2 (response bias - “safe”/”dangerous”) x

2 (communication bias “open”/”closed”)

DV = Trust change

-3

-2

-1

0

1

2

3

Dangerous Safe Dangerous Safe

Tru

st c

hang

e

Correct

Incorrect

Open Closed

Nuclear power

H AC

AC

H

Communication bias

-3

-2

-1

0

1

2

3

Dangerous Safe Dangerous Safe

Tru

st c

hang

e

Correct

Incorrect

Open Closed

Nuclear power

FA

FA

H AC

M

M

AC

H

Communication bias

-3

-2

-1

0

1

2

3

Dangerous Safe Dangerous Safe

Tru

st c

hang

e

Correct

Incorrect

Open Closed

Nuclear power

FA

FA

H AC

M

M

AC

H

Communication bias

-3

-2

-1

0

1

2

3

Dangerous Safe Dangerous Safe

Tru

st c

hang

eCorrect

Incorrect

Open Closed

Travel vaccines

FA

FA

H

AC

M

M

AC

H

Communication bias

-3

-2

-1

0

1

2

3

Dangerous Safe Dangerous Safe

Tru

st c

hang

e

Correct

Incorrect

Open Closed

Computer viruses

FA

FA

H

ACM

M

AC

H

Communication bias

SummaryCorrect decisions (Hits & All Clears) as predicted „False Alarm effect” - Increases in trustClosed Misses - Big falls in trust!

Trust change generalises from exemplar to category

(Specific doctor to doctor in general)

But: 1) Single event (Cry wolf effect?)

2) Might Misses be preferred (e.g. Legal/Rights)

Suicide bombers (with C. Cohrs)

Increase costs of False Alarm (shooting innocent)

Trust in armed police unit following incident

Busy train station, willing to die for the cause

Person either a) Real armed terrorist

b) Someone with mental illness

Almost identical Miami airport last week where marshals shot Rigoberto Alpizar with Bipolar disorder

Piloting - 50% Shoot, 50% Don’t shoot

Suicide bombersOnly “open”DV = 3 item trust scale (= .87) N = 172

Moderation analysis: Right Wing Authoritarianism

(14 item scale related to prejudice and civil rights)

H1: High RWA: Usual pattern

H2: Low RWA: Reverse pattern (Sensitive to costs of FA)

Main analysis

Ability: F(1, 171) = 37.14***Bias: F(1, 171) = 7.07** AxB: F(1, 171) = .38 n.s.

-3

-2

-1

0

1

2

3

"Shoot" "Don't Shoot"

Response bias

Tru

st in

pol

ice

unit

Correct

Incorrect

FA

M

AC

H

-3

-2

-1

0

1

2

3

"Shoot" "Don'tShoot"

"Shoot" "Don'tShoot"

"Shoot" "Don'tShoot"

Right wing authoritarianism

Tru

st in

re

leva

nt p

olic

e

Correct

Incorrect

Moderation Analysis (Bias x RWA F(1,171) = 8.79***)

FAM

AC

HFA

M

AC

H

FA

M

ACH

Very low 0-2.08

(N = 53)

Low 2.09-2.75

(N = 64)

Moderate 2.76-4.42

(N = 54)

Right Wing Authoritarianism

Marginal trust conclusions

1) Trust asymmetry does occur (Bad > Good)

2) In part because of congruency effects

3) But events (rather than polices) still suffer

4) Even some negative events (False Alarms) can lead to increases in trust - but not in all situations and not for all people!

Marginal trust conclusions

If you want to lose trust:

Try to cover up Misses (esp. in a high risk context)

If you want to build trust:

a) Focus on communicating positive policies

b) If you have to talk about events be open

c) And be sensitive to public’s perceptions of costs of benefits of correct/incorrect decisions

General Conclusions (1)

Risk depends on human decisions.Perception of risk involves evaluating

decisions. Decisions can be evaluated in terms of:

Competence (discrimination ability)Partiality (response bias)

Communications can be also evaluated in terms of:

Openness

General Conclusions (2)

Trust is an outcome of such evaluations, plus liking for the decision-maker.

Changes in trust depend on how events are interpreted.

Prior attitudes can guide interpretations.Bad news can have more impact than good.But openness/willingness to admit mistakes

may increase trust.