EITM Institutions Week: Information & Experiments John Aldrich Arthur Lupia.

EITM Institutions Week: Information

& ExperimentsJohn Aldrich

Arthur Lupia

Aldrich Lupia EITM 2007

Can We Trust the Voters?The proposition that [the people] are

the best keeper of their liberties is not true. They are the worst conceivable, they are no keepers at all. They can neither act, judge, think, or will . John Adams, 1788.

“Overall, close to a third of Americans can be categorized as “know-nothings” who are almost completely ignorant of relevant political information – which is not, by any means, to suggest that the other two-thirds are well informed….” Critical Review 1999


Challenge Voters lack details.

Inferences: Common: Voter incompetence. Recent: Voters adapt.

When can people who lack information vote with competence?


Insurance ReformQ: Can badly informed voters use elite endorsements

to emulate the behavior of better-informed voters?

Background High and fast increasing rates. Industry anti-trust exempt. Legislative stalemate. Five competing initiatives. Over $80 million spent. Ralph Nader involved.


Result: Emulation A.Lupia. Am.Pol.Sci.Rev. 88: 63-76. 1994

0 20 40 60 80

Prop 100

Prop 101

Prop 103

Prop 104

Prop 106

LO Info 27 15 26 12.5 45

LO Info +Endorsement

53 5 73 17 12.5

HI Info 53 8 72.5 17 11

Prop 100

Prop 101

Prop 103

Prop 104

Prop 106


Implications Lack of information lack of competence.

To come:

People choose “short cuts” in predictable ways.

Under what conditions do voters use short cuts effectively?


The Democratic DilemmaThe Democratic Dilemma

Can individuals who are not “well informed about political affairs” make the same choices they would have made had they been “well informed?”


Who Believes Whom? Common explanations:

People are sheep. Talk is cheap. Certain attributes required.

E.g., heuristics E.g., reputation, repetition

Common assumptions People know each other. Stimulus/response paradigm. Personal character is the key. External forces do not matter.


Task and ToolsTask: Clarify the political consequences of limited information.

Tools: An exit poll. Game-theoretic models of communication & choice.* Laboratory experiments. Survey experiments. A comparison to other decision makers.

Key Unresolved Question

What if credibility is endogenous?


One way to model uncertainty


The “Standard” Way of Modeling Uncertainty

Is mathematically convenient.

It makes sense if You’ve never cracked the binding of a

introduction to psychology textbook You’ve never attempted to collect data about the

nature of people’s beliefs


A Modeling Tradeoff Continuous message spaces and very

restrictive assumptions about the nature of uncertainty.

Discrete message spaces and far less restrictive assumptions about uncertainty.


Theoretical Premises An uncertain voter makes a binary choice. (1d-wlog)

A speaker says “better” or “worse.” He can lie.

The voter is uncertain about the speaker’s interests and knowledge.

External forces may be present. Verification, Penalties for lying, Observable costly effort.


Model Intuitions Absent external forces, persuasion requires

perceived common interests and perceived speaker knowledge.

The model clarifies how external forces substitute for speaker attributes.

Absent sufficient prior information, competence requires that the perceptions be correct. Institutions can help.


Basic Model Two players: speaker and receiver.

The receiver chooses x or y. Her choice affects both players’ utility.

Sources of uncertainty. Is x or y better for the receiver?

“better” prior: b[0,1]. Do the speaker and receiver have common interests?

“common” prior: c[0,1] Does the speaker have private information about x?

“knows” prior: k[0,1]



Extended Model Penalties for Lying

The speaker pays a penalty for making false statements.

Verification A fourth source of uncertainty emerges. With probability v[0,1], Nature

replaces the speaker’s signal with the true signal.

Costly effort The speaker must pay a positive cost to say anything.

Each exogenous force’s impact on communication strategies and outcomes is determined endogenously.


Prior BeliefsPrior Beliefs


The Effect of a Penalty for LyingThe Effect of a Penalty for Lying


The Effect of Costly EffortThe Effect of Costly Effort



Lab Experiments A subject is a voter or advisor.

The voter predicts coin tosses Earns $1/correct prediction.

Advisor: “heads” or “tails.” we vary perceptions:

hidden die rolls determine speaker interests & knowledge.

we vary “institutions.” penalties for lying, costly effort, verification present in selected

trials.

Persuasion Enlightenment Reasoned Choice

When the Conditions for Persuasion and Enlightenment Were Satisfied

Observed 89 1136/1280

93 1057/1136

83 1067/1280

P redicted 100 100 100 When the Conditions for Pers uas ion Were Not S atis fied Obs erved 58

668/1161 57

383/668 52

600/1161 P redicted 50 50 50

When the Conditions for Pers uas ion Were S atis fied and the Conditions for Enlightenment Were Not

Obs erved 90 104/116

50 52/104

53 62/116

P redicted 100 N/A 50

Summary of Results

From Arthur Lupia and Mathew D. McCubbins. The Democratic Dilemma: Can Citizens Learn What They Need to Know? Ch 7.New York: Cambridge University Press.

50 60 70 80 90 100

NO

PFL

Verif.

Persuasion Reasoned Choice

With sufficient penalty for lying or verification, we expect persuasion and reasoned choice. Otherwise, we do not.

From Arthur Lupia and Mathew D. McCubbins. Elements of Reason: Cognition, Choice and the Bounds of Rationality. Ch. 3.New York: Cambridge University Press.

50 60 70 80 90 100

All

None

P only

Persuasion Reasoned Choice

All: Trials where model predicts persuasion and reasoned choice. None: Model predicts none of the above. P only: Model predicts persuasion only.


CATI Experiment N=1464 “... talk show host [SENDER] [POSITION] spending money to build

more prisons. What do you think? Is spending money to build prisons a good idea or a bad idea?”

“How much would you say that [SENDER] knows about what will happen if this country spends money to build more prisons -- a lot, some, a little, or nothing?”

“On most political issues would you say that you and [SENDER] agree all of the time, most of the time, only some of the time, or never?”

Random Selection

No Speaker20%

CONTEXT 1Limbaugh

20%

CONTEXT 2Donahue

20%

Message Content: Supports

CONTEXT 1Limbaugh

20%

CONTEXT 2Donahue

20%

Message Content: Opposes

RESPONDENT

From Arthur Lupia and Mathew D. McCubbins. The Democratic Dilemma: Can Citizens Learn What They Need to Know? Ch 8.New York: Cambridge University Press.

Metric: (%Yes|Heard Supports) - (%No|Heard Supports)

L to R: declining perceptions of trust and knowledge (K>k); we expect declining effects.

-40

-30

-20

-10

0

10

20

30

40

AK aK Mid ~ak

LimbaughEffect ofTreatment

Donahue Effectof Treatment

Just Ideology?

Factors such as ideology depend on perceived agreement and knowledge. The converse is not true.

Perceived knowledge & trust are the fundamental source effects.

-30-20

-10

010

20

3040

50

AK aK Other

SameIdeology

DifferentIdeology


Implications Citizens process political information in systematic ways.

Strategic considerations matter.

People can choose competently despite lacking details. True in a range of experimental environments. Would electoral outcomes be different today?

Results imply different solutions. Circulate endorsement information. Make it easier to “follow the money.” Complexity increases requirements.

Were Bush Tax Cut Supporters “Simply Ignorant?” A Second

Look at Conservatives and Liberals in “Homer Gets a Tax

Cut”

Arthur Lupia, Adam Seth Levine, Jesse O. Menning, Gisela Sin

Perspectives on Politics - Nov 2008


“Homer Gets a Tax Cut” “[T]he strong plurality support for Bush’s tax cut…

is entirely attributable to simple ignorance.” Bartels (2005:24)

Using the same data, we show that this claim is untrue.


One View of the Impact of Knowledge

All respondents

0.7 0.69 0.67

0

0.2

0.4

0.6

0.8

1

Very Low toAverage (345)

Fairly High(321)

Very High (208)

Interviewer Information Rating (post)

Proportion supporting

Dependent variable: Support for/Opposition to the Tax Cut


And Another

Republicans

0.893 0.909 0.962

0

0.2

0.4

0.6

0.8

1


Fairly High(175)

Very High (107)



Over 96% of the most informed Republicans supported the tax cut.

Democrats

0.542

0.3930.348

0

0.2

0.4

0.6

0.8

1


Fairly High(135)

Very High (89)




Multivariate Analysis: “Homer” vs. Partisans

Variable “Homer” Dems Reps

Political Information (0 to 1)

-.907(.353)

-1.567(.492)

.102(.461)

Republican Party ID (-1 to 1)

.759(.055)

Family Income (in 1000s)

.0002(.001)

.002(.002)

.000(.001)

“President Bush” wording

-.080(.049)

-.072(.086)

-.032(.051)

Constant .873(.153)

1.013(.308)

.648(.316)

Obs 896 387 418

Dependent variable: Support for/Opposition to the Tax Cut


Conclusion The “Homer” result is due to:

Presuming that people with different worldviews react to “more information” in the same way.

The fact that liberal opinions on this issue varied far more than those of other respondents.

Examined several ways, it appears that much of the support for the tax cut is attributable to something other than simple ignorance.

Money, Time and the Ability to Answer Political Knowledge

QuestionsMarkus Prior and Arthur Lupia

AJPS - January 2008


Research Design A two-by-two design. Factor 1: Time

Control: no incentive Treatement: $1 per correct answer

Factor 2: Money Control: 60 seconds per question Treatment: 24 hours to complete all questions


Research Design Embedded in representative survey

conducted by Knowledge Networks Oct. 19 – Nov. 1, 2004 N = 1,550 (1,220 completes, 79%)

The questions 7 on political issues, 7 on economic issues about information relevant to the 2004 election


Prior/Lupia resultsNo incentive $1/correct

answer

<60s/Q control 11% increase

<24h/14 Qs 18% increase 24% increase


Why, and for what class of problems, are Nash-based concepts

satisfactory?


Question 1

Q: Do people, such as voters, think about politics in ways that popular equilibrium concepts imply.

A: Many do not.


Question 2 Q: Should differences in reasoning affect game-

theoretic political science?

A: It depends. - In some cases, the “as if” assumption is difficult to justify.

What is the cognitive basis of game-theoretic political science?


Nash Equilibrium

All players share the same counterfactual assessment along the equilibrium path. Two players cannot disagree about the play of a third.

Gibbons (1992: 8-9)


SCE Components A finite number of players (iI) have types () and take

actions (a A). i:i(Ai), a probability distribution over actions.

Common knowledge: players know their own utility functions & the extensive form. They need not know others’ strategies or the distribution of

Nature’s type.


SCE Requirements Condition i states that each player has correct

beliefs about her own type.

Condition ii states that any action that a player plays with positive probability must maximize her utility given her conjecture about other players and Nature.

Condition iii requires that players’ observations be consistent with their conjectures.


All important beliefs i() is player i’s prior belief about Nature’s

type. Players need not have correct (i=p) or common (i= j)

priors.

yi=yi(a,) is player i’s private signal about the play of the game. The quality of the private signal is a key variable in SCE

models.


SCE Definition

True type distribution

True strategies

Conjecture about types

Conjecture about actions


SCE Properties Observations and conjectures must be

consistent.Incorrect conjectures are allowed.

Two players can disagree about a third (or Nature).

Bayesian updating is not required.

More precise observations imply greater constraints.


Congress-President Standoff

If C or P “stand off”, gov’t shuts down. If gov’t shuts down, V assigns blame. The outcome (~S, ~S) is a SCE.

Each believes that the voter will blame the other for a standoff. The outcome (~S, ~S) is not a BNE.

Any voter strategy induces at least one other actor to continue the standoff.

Adapted from Fudenberg and Kreps 1995.


Example: Thinking Differently in Jury Theorems Condorcet (1785): There are N jurors. Each chooses guilt or

innocence. One verdict is correct, If each juror votes correctly with probability >.5, the probability that a majority votes correctly goes to 1 as N.

Austen-Smith and Banks (1996): Each juror receives an independent evidentiary signal. If she uses Bayes Rule and has certain kinds of priors about other jurors, voting in accordance with her signal is not an equilibrium. “If I am pivotal, then…”


Example: Thinking Differently in Jury Theorems Feddersen and Pesendorfer (1785): Extends Austen-Smith

and Banks to unanimous verdicts. Jurors will vote against “innocent” evidentiary signals. Unanimous verdicts do not minimize false convictions.

Guernaschelli, McKelvey, and Palfrey (2000): Experiments of F&P offer mixed support. “Feddersen and Pesendorfer (1998) imply that large unanimous

juries will convict innocent defendants with fairly high probability… this did not happen in our experiment.”

The gap between the theoretical prediction and the experimental data grew with jury size.


Empirical Studies Pennington and Hastie “story model”

Each juror attempts to make sense of the evidence by assembling it into a narrative format.

“many jurors tended to construct only one of the possible stories,”

“jurors were surprised to discover that there were other possible stories”

Cacioppo and Petty “need for cognition” While some citizens enjoy dealing with logical

abstractions, others strive to minimize the mental effort devoted to such activities.


Our model (a variant of F&P) There are with N={1,2,…n} jurors.

Let ={G,I}, where =G means that the defendant committed the crime in question and =I means that he did not.

Each evidentiary signal is an independent Bernoulli random variable, mj{g,i}, which, for each juror j, reveals the true value of with probability p(.5,1).

After observing mj, each juror casts a vote Xj{A,C}, where Xj=A is a vote by juror j to acquit and Xt=C is a vote to convict.

If all n jurors choose C, then the group decision is C; otherwise it is A. All jurors prefer to convict only the guilty and to set only the innocent free.


Our model allows different Juror Types

Findings Letting high and low NFC

jurors think in different ways drives this same probability to zero very quickly as they jury grows.

To understand how often unanimity rule convicts the innocent, we need to know more about how jurors think.


Conclusion Choosing an equilibrium concept is equivalent to making

an assumption about how actors reason in equilibrium.

Nash family equilibrium concepts impose a rigorous kind of reasoning.

Concepts, such as SCE, may be more appropriate for actors who have limited ability or incentive to contemplate others’ strategies.


Challenges The SCE agenda poses an important challenge to

the unseen assumptions about cognition that support Nash-based inferences.

The agenda does not fill in the theory of beliefs and belief change that is needed. A place where greater integration of empirical and

theoretical work can open great windows of opportunity.


Experimental Design:A Close Analogy of our Theory

Our experiments contained a principal and a speaker.

The principal’s job was to predict coin toss outcomes. The speaker made statements.

We paid each principal $1 for each correct prediction.

We varied the speaker’s knowledge, interests, and incentives. Die roll to determine interests, knowledge. Penalties for lying, costly effort, verification.

These variations defined our treatment and control conditions and were the means for evaluating our theory.


Theoretical Premises and Experimental Analogies


Other design elements. Quiz on instructions.

Means of communication controlled.

Multiple interactions -- independent payoffs, no added information about prior periods, no reputation effects.

Multiple principals -- independent payoffs for principals, no way for speaker to distinguish principals.


Lab Experiments A subject is a voter or advisor.

The voter predicts coin tosses Earns $1/correct prediction.

Advisor: “heads” or “tails.” we vary perceptions:

hidden die rolls determine speaker interests & knowledge.

we vary “institutions.” penalties for lying, costly effort, verification present in selected trials.


Sequence of Events Experimental Series 3

We flip the coin.

We show the results to the reporter.

The reporter makes a statement.

The predictors make their predictions.

If the predictor makes a correct prediction, then both the Predictor and the Reporter earn $.50.

Apart from this, a predictor earns $.05 for predicting heads.


Sequence of Events Experimental Series 4 We flip the coin.

There is a 70% chance that we show the result to the reporter.







The reporter makes a statement without seeing the coin flip.






The reporter makes a statement without seeing the coin flip.


If the Predictor makes a correct prediction, then the Predictor earns $.50 and the Reporter earn nothing.

If the Predictor makes and incorrect prediction, then the Predictor earns nothing and the Reporter earns $.50.




We show the result to the reporter.





Sequence of Events Experimental Series 8 We roll the dice. If it lands 1 through 7, the Reporter is

paid $.50 every time a predictor makes a correct prediction. If it lands 8 through 10, then the reporter is paid $.50 every time a predictor makes an incorrect prediction.

We flip the coin.





Sequence of Events Experimental Series 9 We roll the dice. If it lands 1 or 2, the Reporter is paid $.50

every time a predictor makes a correct prediction. If it lands 3 through 10, then the reporter is paid $.50 every time a predictor makes an incorrect prediction.

We flip the coin.





Your trials Complete information. + Incomplete information. No advice. ~ Expertise & Common Interests. + Expertise & Conflicting Interests. ~ No expertise & Common Interests. ~ No expertise & Conflicting Interests. ~ Expertise & Common Interests. + Expertise & 70% Common Interests. Persuasive. Expertise & 30% Common Interests. ~

EITM Institutions Week: Information & Experiments John Aldrich Arthur Lupia.

Documents

speaker attributes

speaker knowledge

information vote

perceived common interests

external forces substitute

common explanations

nature of uncertainty

dwloga speaker