questions in decision theory - tauigilboa/personal/presentations... · a decision maker is de–ned...
TRANSCRIPT
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Questions in Decision Theory
Itzhak Gilboa
June 15, 2011
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History
Pascal and Bernoulli
Ramsey and deFinetti
von Morgenstern-Neumann
Savage
Anscombe-Aumann
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History
Pascal and Bernoulli
Ramsey and deFinetti
von Morgenstern-Neumann
Savage
Anscombe-Aumann
Gilboa () Questions in Decision Theory June 15, 2011 2 / 18
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History
Pascal and Bernoulli
Ramsey and deFinetti
von Morgenstern-Neumann
Savage
Anscombe-Aumann
Gilboa () Questions in Decision Theory June 15, 2011 2 / 18
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History
Pascal and Bernoulli
Ramsey and deFinetti
von Morgenstern-Neumann
Savage
Anscombe-Aumann
Gilboa () Questions in Decision Theory June 15, 2011 2 / 18
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History
Pascal and Bernoulli
Ramsey and deFinetti
von Morgenstern-Neumann
Savage
Anscombe-Aumann
Gilboa () Questions in Decision Theory June 15, 2011 2 / 18
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The Bible (Savage, 1954)
F = X S = ff j f : S ! Xg
P1 % is a weak order
P2 f hAc % ghAc i¤ f h0
Ac % gh0Ac
P3 x % y i¤ f xA % fyA
P4 y xA % y xB i¤ w zA % w zBP5 9 f � gP6 f � g 9 a partition of S , fA1, ...,Ang f hAi � g and f � ghAi
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The Bible (Savage, 1954)
F = X S = ff j f : S ! XgP1 % is a weak order
P2 f hAc % ghAc i¤ f h0
Ac % gh0Ac
P3 x % y i¤ f xA % fyA
P4 y xA % y xB i¤ w zA % w zBP5 9 f � gP6 f � g 9 a partition of S , fA1, ...,Ang f hAi � g and f � ghAi
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The Bible (Savage, 1954)
F = X S = ff j f : S ! XgP1 % is a weak order
P2 f hAc % ghAc i¤ f h0
Ac % gh0Ac
P3 x % y i¤ f xA % fyA
P4 y xA % y xB i¤ w zA % w zBP5 9 f � gP6 f � g 9 a partition of S , fA1, ...,Ang f hAi � g and f � ghAi
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The Bible (Savage, 1954)
F = X S = ff j f : S ! XgP1 % is a weak order
P2 f hAc % ghAc i¤ f h0
Ac % gh0Ac
P3 x % y i¤ f xA % fyA
P4 y xA % y xB i¤ w zA % w zBP5 9 f � gP6 f � g 9 a partition of S , fA1, ...,Ang f hAi � g and f � ghAi
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The Bible (Savage, 1954)
F = X S = ff j f : S ! XgP1 % is a weak order
P2 f hAc % ghAc i¤ f h0
Ac % gh0Ac
P3 x % y i¤ f xA % fyA
P4 y xA % y xB i¤ w zA % w zB
P5 9 f � gP6 f � g 9 a partition of S , fA1, ...,Ang f hAi � g and f � ghAi
Gilboa () Questions in Decision Theory June 15, 2011 3 / 18
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The Bible (Savage, 1954)
F = X S = ff j f : S ! XgP1 % is a weak order
P2 f hAc % ghAc i¤ f h0
Ac % gh0Ac
P3 x % y i¤ f xA % fyA
P4 y xA % y xB i¤ w zA % w zBP5 9 f � g
P6 f � g 9 a partition of S , fA1, ...,Ang f hAi � g and f � ghAi
Gilboa () Questions in Decision Theory June 15, 2011 3 / 18
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The Bible (Savage, 1954)
F = X S = ff j f : S ! XgP1 % is a weak order
P2 f hAc % ghAc i¤ f h0
Ac % gh0Ac
P3 x % y i¤ f xA % fyA
P4 y xA % y xB i¤ w zA % w zBP5 9 f � gP6 f � g 9 a partition of S , fA1, ...,Ang f hAi � g and f � ghAi
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Savage�s Theorem
Assume that X is �nite. Then % satis�es P1-P6 if and only if thereexist a non-atomic �nitely additive probability measure µ on S(=(S , 2S )) and a non-constant function u : X ! R such that, forevery f , g 2 F
f % g i¤ZSu(f (s))dµ(s) �
ZSu(g(s))dµ(s)
Furthermore, in this case µ is unique, and u is unique up to positivelinear transformations.
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Decision Theory at a Crossroad
Accuracy vs. beauty/generality
Method: experiments, axioms, neurological data?
Goal: theoretical models or applied decisions?
Descriptive or normative?
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Decision Theory at a Crossroad
Accuracy vs. beauty/generality
Method: experiments, axioms, neurological data?
Goal: theoretical models or applied decisions?
Descriptive or normative?
Gilboa () Questions in Decision Theory June 15, 2011 5 / 18
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Decision Theory at a Crossroad
Accuracy vs. beauty/generality
Method: experiments, axioms, neurological data?
Goal: theoretical models or applied decisions?
Descriptive or normative?
Gilboa () Questions in Decision Theory June 15, 2011 5 / 18
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Decision Theory at a Crossroad
Accuracy vs. beauty/generality
Method: experiments, axioms, neurological data?
Goal: theoretical models or applied decisions?
Descriptive or normative?
Gilboa () Questions in Decision Theory June 15, 2011 5 / 18
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Main Questions
Rationality
Probability
Utility
Rules and analogies
Group decisions
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Main Questions
Rationality
Probability
Utility
Rules and analogies
Group decisions
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Main Questions
Rationality
Probability
Utility
Rules and analogies
Group decisions
Gilboa () Questions in Decision Theory June 15, 2011 6 / 18
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Main Questions
Rationality
Probability
Utility
Rules and analogies
Group decisions
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Main Questions
Rationality
Probability
Utility
Rules and analogies
Group decisions
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Rationality
Older concept: �Rational Man� should do...
In neoclassical economics: only consistency
An even more subjective view: which consistency?
Rationality as robustness
Weaknesses (?): subjective, empirical, not monotonic in intelligence
Defense
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Rationality
Older concept: �Rational Man� should do...
In neoclassical economics: only consistency
An even more subjective view: which consistency?
Rationality as robustness
Weaknesses (?): subjective, empirical, not monotonic in intelligence
Defense
Gilboa () Questions in Decision Theory June 15, 2011 7 / 18
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Rationality
Older concept: �Rational Man� should do...
In neoclassical economics: only consistency
An even more subjective view: which consistency?
Rationality as robustness
Weaknesses (?): subjective, empirical, not monotonic in intelligence
Defense
Gilboa () Questions in Decision Theory June 15, 2011 7 / 18
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Rationality
Older concept: �Rational Man� should do...
In neoclassical economics: only consistency
An even more subjective view: which consistency?
Rationality as robustness
Weaknesses (?): subjective, empirical, not monotonic in intelligence
Defense
Gilboa () Questions in Decision Theory June 15, 2011 7 / 18
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Rationality
Older concept: �Rational Man� should do...
In neoclassical economics: only consistency
An even more subjective view: which consistency?
Rationality as robustness
Weaknesses (?): subjective, empirical, not monotonic in intelligence
Defense
Gilboa () Questions in Decision Theory June 15, 2011 7 / 18
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Rationality
Older concept: �Rational Man� should do...
In neoclassical economics: only consistency
An even more subjective view: which consistency?
Rationality as robustness
Weaknesses (?): subjective, empirical, not monotonic in intelligence
Defense
Gilboa () Questions in Decision Theory June 15, 2011 7 / 18
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Objective and Subjective Rationality
A decision maker is de�ned by two relations�%�,%^
�
%� �can convince �any reasonable decision maker� that it is right%^ �cannot be convinced that it is wrongClearly, %��%^
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Objective and Subjective Rationality
A decision maker is de�ned by two relations�%�,%^
�%� �can convince �any reasonable decision maker� that it is right
%^ �cannot be convinced that it is wrongClearly, %��%^
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Objective and Subjective Rationality
A decision maker is de�ned by two relations�%�,%^
�%� �can convince �any reasonable decision maker� that it is right%^ �cannot be convinced that it is wrong
Clearly, %��%^
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Objective and Subjective Rationality
A decision maker is de�ned by two relations�%�,%^
�%� �can convince �any reasonable decision maker� that it is right%^ �cannot be convinced that it is wrongClearly, %��%^
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Classical and Bayesian Statistics
Classical: attempts to be objective, no intuition
Bayesian: attempts to incorporate intuition and hunches
Classical � for making a point (to others)
Bayesian � for making a decision (for oneself)
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Classical and Bayesian Statistics
Classical: attempts to be objective, no intuition
Bayesian: attempts to incorporate intuition and hunches
Classical � for making a point (to others)
Bayesian � for making a decision (for oneself)
Gilboa () Questions in Decision Theory June 15, 2011 9 / 18
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Classical and Bayesian Statistics
Classical: attempts to be objective, no intuition
Bayesian: attempts to incorporate intuition and hunches
Classical � for making a point (to others)
Bayesian � for making a decision (for oneself)
Gilboa () Questions in Decision Theory June 15, 2011 9 / 18
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Classical and Bayesian Statistics
Classical: attempts to be objective, no intuition
Bayesian: attempts to incorporate intuition and hunches
Classical � for making a point (to others)
Bayesian � for making a decision (for oneself)
Gilboa () Questions in Decision Theory June 15, 2011 9 / 18
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Probability
What is the probability of
A coin coming up Head?
A car being stolen?
A surgery succeeding?
A war erupting?
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Probability
What is the probability of
A coin coming up Head?
A car being stolen?
A surgery succeeding?
A war erupting?
Gilboa () Questions in Decision Theory June 15, 2011 10 / 18
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Probability
What is the probability of
A coin coming up Head?
A car being stolen?
A surgery succeeding?
A war erupting?
Gilboa () Questions in Decision Theory June 15, 2011 10 / 18
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Probability
What is the probability of
A coin coming up Head?
A car being stolen?
A surgery succeeding?
A war erupting?
Gilboa () Questions in Decision Theory June 15, 2011 10 / 18
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Probability
What is the probability of
A coin coming up Head?
A car being stolen?
A surgery succeeding?
A war erupting?
Gilboa () Questions in Decision Theory June 15, 2011 10 / 18
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Subjective probability
Relying on remarkable foundations (Ramsey, de Finetti, Savage,Anscombe-Aumann)
Yet problematic:
Descriptively: people violate axioms (Ellsberg)
Normatively: completeness?
Back to rationality: if it�s so rational, why isn�t it objective?
The Bayesian approach is good at representing knowledge, poor atrepresenting ignorance
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Subjective probability
Relying on remarkable foundations (Ramsey, de Finetti, Savage,Anscombe-Aumann)
Yet problematic:
Descriptively: people violate axioms (Ellsberg)
Normatively: completeness?
Back to rationality: if it�s so rational, why isn�t it objective?
The Bayesian approach is good at representing knowledge, poor atrepresenting ignorance
Gilboa () Questions in Decision Theory June 15, 2011 11 / 18
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Subjective probability
Relying on remarkable foundations (Ramsey, de Finetti, Savage,Anscombe-Aumann)
Yet problematic:
Descriptively: people violate axioms (Ellsberg)
Normatively: completeness?
Back to rationality: if it�s so rational, why isn�t it objective?
The Bayesian approach is good at representing knowledge, poor atrepresenting ignorance
Gilboa () Questions in Decision Theory June 15, 2011 11 / 18
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Subjective probability
Relying on remarkable foundations (Ramsey, de Finetti, Savage,Anscombe-Aumann)
Yet problematic:
Descriptively: people violate axioms (Ellsberg)
Normatively: completeness?
Back to rationality: if it�s so rational, why isn�t it objective?
The Bayesian approach is good at representing knowledge, poor atrepresenting ignorance
Gilboa () Questions in Decision Theory June 15, 2011 11 / 18
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Subjective probability
Relying on remarkable foundations (Ramsey, de Finetti, Savage,Anscombe-Aumann)
Yet problematic:
Descriptively: people violate axioms (Ellsberg)
Normatively: completeness?
Back to rationality: if it�s so rational, why isn�t it objective?
The Bayesian approach is good at representing knowledge, poor atrepresenting ignorance
Gilboa () Questions in Decision Theory June 15, 2011 11 / 18
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Subjective probability
Relying on remarkable foundations (Ramsey, de Finetti, Savage,Anscombe-Aumann)
Yet problematic:
Descriptively: people violate axioms (Ellsberg)
Normatively: completeness?
Back to rationality: if it�s so rational, why isn�t it objective?
The Bayesian approach is good at representing knowledge, poor atrepresenting ignorance
Gilboa () Questions in Decision Theory June 15, 2011 11 / 18
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Objective probabilities
Exist in simple cases (iid)
Can be de�ned with identicality, as long as causal independence isretained
Rule-based approaches: logit
Case-based approaches: empirical similarity
But none extends to the cases of wars, stock market crashes...
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Objective probabilities
Exist in simple cases (iid)
Can be de�ned with identicality, as long as causal independence isretained
Rule-based approaches: logit
Case-based approaches: empirical similarity
But none extends to the cases of wars, stock market crashes...
Gilboa () Questions in Decision Theory June 15, 2011 12 / 18
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Objective probabilities
Exist in simple cases (iid)
Can be de�ned with identicality, as long as causal independence isretained
Rule-based approaches: logit
Case-based approaches: empirical similarity
But none extends to the cases of wars, stock market crashes...
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Objective probabilities
Exist in simple cases (iid)
Can be de�ned with identicality, as long as causal independence isretained
Rule-based approaches: logit
Case-based approaches: empirical similarity
But none extends to the cases of wars, stock market crashes...
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Objective probabilities
Exist in simple cases (iid)
Can be de�ned with identicality, as long as causal independence isretained
Rule-based approaches: logit
Case-based approaches: empirical similarity
But none extends to the cases of wars, stock market crashes...
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Alternatives to the Bayesian approach
Schmeidler (1989): non-additive probabilities (capacities)
Integration by Choquet�s integral
Maxmin EU: there exists a set of probabilities C such that
V (f ) = minP2C
ZSu (f (s)) dP (s)
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Alternatives to the Bayesian approach
Schmeidler (1989): non-additive probabilities (capacities)
Integration by Choquet�s integral
Maxmin EU: there exists a set of probabilities C such that
V (f ) = minP2C
ZSu (f (s)) dP (s)
Gilboa () Questions in Decision Theory June 15, 2011 13 / 18
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Alternatives to the Bayesian approach
Schmeidler (1989): non-additive probabilities (capacities)
Integration by Choquet�s integral
Maxmin EU: there exists a set of probabilities C such that
V (f ) = minP2C
ZSu (f (s)) dP (s)
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Other multiple-priors models
Nau, Klibano¤-Marinacci-Mukerji: �smooth preferences�
ϕ : R ! RZ∆(S )
ϕ
�Zu(f ) dp
�dµ
Maccheroni-Marinacci-Rustichini: �variational preferences�
V (f ) = minP2∆(S )
�ZSu (f (s)) dP (s) + c(P)
�
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Other multiple-priors models
Nau, Klibano¤-Marinacci-Mukerji: �smooth preferences�
ϕ : R ! RZ∆(S )
ϕ
�Zu(f ) dp
�dµ
Maccheroni-Marinacci-Rustichini: �variational preferences�
V (f ) = minP2∆(S )
�ZSu (f (s)) dP (s) + c(P)
�
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Incomplete relation
Bewley:
f � g
i¤
8p 2 CZSu (f (s)) dP (s) >
ZSu (g (s)) dP (s)
Fits the �objective rationality�notion
Can be combined with the maxmin criterion as �subjective rationality�
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Incomplete relation
Bewley:
f � g
i¤
8p 2 CZSu (f (s)) dP (s) >
ZSu (g (s)) dP (s)
Fits the �objective rationality�notion
Can be combined with the maxmin criterion as �subjective rationality�
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Incomplete relation
Bewley:
f � g
i¤
8p 2 CZSu (f (s)) dP (s) >
ZSu (g (s)) dP (s)
Fits the �objective rationality�notion
Can be combined with the maxmin criterion as �subjective rationality�
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Utility
What is utility and how is it related to well-being or happiness?
Measurement of well-being and its relation to money
The paraplegics and lottery winners
Problems of measurement
All happy families... ?
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Utility
What is utility and how is it related to well-being or happiness?
Measurement of well-being and its relation to money
The paraplegics and lottery winners
Problems of measurement
All happy families... ?
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Utility
What is utility and how is it related to well-being or happiness?
Measurement of well-being and its relation to money
The paraplegics and lottery winners
Problems of measurement
All happy families... ?
Gilboa () Questions in Decision Theory June 15, 2011 16 / 18
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Utility
What is utility and how is it related to well-being or happiness?
Measurement of well-being and its relation to money
The paraplegics and lottery winners
Problems of measurement
All happy families... ?
Gilboa () Questions in Decision Theory June 15, 2011 16 / 18
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Utility
What is utility and how is it related to well-being or happiness?
Measurement of well-being and its relation to money
The paraplegics and lottery winners
Problems of measurement
All happy families... ?
Gilboa () Questions in Decision Theory June 15, 2011 16 / 18
![Page 67: Questions in Decision Theory - TAUigilboa/Personal/Presentations... · A decision maker is de–ned by two relations % %, ^ % Œcan convince fiany reasonable decision makerflthat](https://reader034.vdocuments.net/reader034/viewer/2022051910/60000c3b31da7b01b22d6a2e/html5/thumbnails/67.jpg)
Rules and analogies
In the context of probability
Statistics
Moral argumentation
Recent model unifying the two, as well as Bayesian
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Rules and analogies
In the context of probability
Statistics
Moral argumentation
Recent model unifying the two, as well as Bayesian
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Rules and analogies
In the context of probability
Statistics
Moral argumentation
Recent model unifying the two, as well as Bayesian
Gilboa () Questions in Decision Theory June 15, 2011 17 / 18
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Rules and analogies
In the context of probability
Statistics
Moral argumentation
Recent model unifying the two, as well as Bayesian
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![Page 71: Questions in Decision Theory - TAUigilboa/Personal/Presentations... · A decision maker is de–ned by two relations % %, ^ % Œcan convince fiany reasonable decision makerflthat](https://reader034.vdocuments.net/reader034/viewer/2022051910/60000c3b31da7b01b22d6a2e/html5/thumbnails/71.jpg)
Group decisions
Do groups make better decisions than do individuals?
�Truth wins� vs. risk/uncertainty aversion
Aggregation of opinions/judgment aggregation
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Group decisions
Do groups make better decisions than do individuals?
�Truth wins� vs. risk/uncertainty aversion
Aggregation of opinions/judgment aggregation
Gilboa () Questions in Decision Theory June 15, 2011 18 / 18
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Group decisions
Do groups make better decisions than do individuals?
�Truth wins� vs. risk/uncertainty aversion
Aggregation of opinions/judgment aggregation
Gilboa () Questions in Decision Theory June 15, 2011 18 / 18