a dynamic stochastic framework for dual process models€¦ · overview motivation and background...
TRANSCRIPT
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A dynamic stochastic framework for dual process models
Adele Diederich
Jacobs University Bremen
Purdue Winer Memorial Lectures
November 9 – 12, 2018
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Overview
Motivation and Background
Basic assumptions of sequential sampling models
Dynamic Dual Process model framework
Predictions
Variations
Model account for data
Further directions
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De Martino, Kumaran, Seymour, & Dolan (2006), Science
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Framing effect
Framing effect: cognitive bias, in which people react to a particularchoice in different ways depending on how it is presented; e.g. as a loss oras a gain.
Preference reversal
Shift in preference
(cf. externality, description-invariance)
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Risky choice framing
Choice between two options
Lotteries
Option A is typically riskless
Option B is risky
Situation 1 Outcomes are framed as gains (positive frame)
Situation 2 Outcomes are framed as losses (negative frame)
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Gain frame
Keep
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Loss frame
Lose
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.
Kahneman & Tversky (1979), Prospect Theory (PT), reflectioneffect
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De Martino et al. (2006)
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De Martino et al. (2006)
Increased activation in the amygdala was associated with subjects’tendency to be risk-averse in the Gain frame and risk-seeking in theLoss frame, supporting the hypothesis that the framing effect isdriven by an affect heuristic underwritten by an emotional system.
When subjects’ choices ran counter to their general behavioraltendency, there was enhanced activity in the anterior cingulate cortex(ACC). This suggests an opponency between two neural systems,with ACC activation consistent with the detection of conflict betweenpredominantly ”analytic” response tendencies and a more”emotional” amygdala-based system.
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Loewenstein, O’Donoghue, & Bhatia (2015), Decision
”Evolutionarily older brain regions, such as the limbic system, whichincludes areas such as the amygdala and the hypothalamus, evolvedto promote survival and reproduction, incorporate affectivemechanisms (MacLean, 1990). In contrast, the seemingly uniquehuman ability to choose deliberately, by focusing on broader goals,relies on the prefrontal cortex (Damasio, 1994; Lhermitte, 1986;Miller & Cohen, 2001), the region of the brain that expanded mostdramatically in the course of human evolution (Manuck et al., 2003).Indeed, these results have led to dual-system frameworks for theneuroscience of decision making. ”
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Loewenstein et al. (2015)
Framework for intertemporal choice, risky decisions, and social preferences
Affective system
Deliberate system
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Dual process models
System 1 Intuitive (fast, emotional, parallel, holistic, . . . )
System 2 Deliberate (slow, rational, serial, . . .)
Applied to cognitive processes including reasoning and judgments
Problems for most approaches:
Verbal – allows no quantitative predictions
Unclear about processing
Reverse inference
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New modeling approach
Multi-stage decision model aka multiattribute attention switching(MAAS) model
Belongs to the class of sequential sampling models
Predictions of choice response times and choice frequencies
Linked to ”fact” in biology (natural construct): evidenceaccumulation and threshold
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Sequential sampling approach
The information built-up or evidence or preference is incrementedaccording to a diffusion process:
dX (t) = µ(X (t), t)dt + σ(X (t), t)dW (t)
The effective drift rate µ(x , t) describes the instantaneous rate ofexpected increment change at time t and state x = X (t).
The diffusion coefficient σ(x , t) in front of the instantaneousincrements dW (t) of a standard Wiener process W (t) relates to thevariance of the increments.
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Sequential sampling approach – Basic assumptions
0
Criterion for choosing Sure
Criterion for choosing Gamble
time (t) P(t
)
Preference sampledcontinuously over time
Random fluctuation inaccumulating preferencestrength
P(t) stochastic process
Each trajectory representsthe accumulation process forone trial
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Basic assumptions – Starting point
0
Criterion for choosing Sure
Criterion for choosing Gamble
time (t) P(t
)
Initial state of preferencestrength P(0)
P(0) = 0 : neutral
P(0) > 0 : favoring Gamble
P(0) < 0 : favoring Sure
Fixed position → initialstate z
Random location → initialdistribution Z
A priori bias
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Basic assumptions – Increments
0
Criterion for choosing Sure
Criterion for choosing Gamble
time (t) P(t
)
Increments of preferencesampled at any moment intime dP(t)
dP(t) > 0 : favoringGamble at t
dP(t) < 0 : favoring Sure att
Continuous update ofpreference
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Basic assumptions – Decision criterion
0
Criterion for choosing Sure
Criterion for choosing Gamble
time (t) P(t
)
Process stops and responseis initiated when a criterionis reached
Instructions or strategiesaffects the criterion
Function of time constraints
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Basic assumptions – Thresholds: Stopping times
0
Criterion for choosing Sure
Criterion for choosing Gamble
time (t) P(t
)
Optional stopping tIme
P(t) = θG > 0 – choose G
P(t) = θS < 0 – choose S
Internally controlled decisionthreshold
0 t
Acc
umul
atio
n P
roce
ss X
(t)
Fixed Stopping Time
Alternative A
Alternative B
Fixed stopping time
Externally controlleddecision threshold
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Variable decision boundaries
Diederich & Oswald (2016) Multi-stage sequential sampling modelswith finite or infinite time horizon and variable boundaries. Journal ofMathematical Psychology
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Basic assumptions – Drift rate
0
Criterion for choosing Sure
Criterion for choosing Gamble
time (t)P(t
)
Quality of evidencedetermines drift rate (meandrift, drift coefficient)
The better the evidence thelarger µ
The better the alternativescan be discriminated thehigher the the drift rate.
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Multi-stage decision model: Basic assumptions
For each system a different accumulation process takes place.
Attention switches from one system to the other system and the twosystems are processed serially
Alternative: The two systems are processes in parallel.
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Preference Process
Preference strength is updated from one moment, t, to the next, (t + h)by an reflecting the momentary comparison of consequences produced byimagining the choice of either option G or S with
P(t + h) = P(t) + Vi (t + h),
V (t) : input valence
V (t) = V G (t)− V S(t)
V G (t) : momentary valence for the gamble
V S(t) : momentary valence for the sure option
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Gain and Loss frame
E (S) = E (G )Gain frame
P(t
)
0
θS
θG
t
t1
System 1 System 2
Loss frame
P(t
)
0
θS
θG
t
t1
System 1 System 2
Attention switches from system 1 to system 2 at time t1
Switching time may be deterministic or random (according todistribution)
Solid lines indicate the drift rates
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Predictions
Prediction 1: The size of the framing effect is a function of the timethe DM operates in System 1.
Prediction 2: The size of the framing effect is a function of the time(limit) the DM has for making a choice.
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Prediction 1: Attention times
The size of the framing effect is a function of the time the DM operates inSystem 1.
0
θS
θG
t
t1
System 1 System 2
P(t
)
0
θS
θG
t
t1
System 1 System 2
P(t
)
Gain frameSystem 1: drift rate < 0→ SureSystem 2: drift rate = 0→ indifferent between Gamble and Sure
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Prediction 2: Deadlines
The size of the framing effect is a function of the time (limit) the DM hasfor making a choice.
0
θS
θG
t
t1
System 1 System 2
P(t
)
P(t
)
0
θS
θG
t
t1
System 1 System 2
Gain frameSystem 1: drift rate < 0→ SureSystem 2: drift rate = 0→ indifferent between Gamble and Sure
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In the following
WienerdV (t) = δidt + σidW (t)
with
drift rate δi , i = 1, 2, one for each system
diffusion coefficient σ2i = 1
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Modeling the drift rates – Example 1
System 1Preferences in System 1 are constructed according to prospecttheory
System 2Preferences in System 2 are constructed according to expectedutility theory
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System 1: Prospect Theory
The value V of a simple prospect that pays x (here the startingamount) with probability p (and nothing otherwise) is given by:
V(x , p) = w(p)v(x)
with probability weighting function
w(p) =pγ
(pγ + (1− p)γ)1/γ
and value function
v(x) =
{xα if x ≥ 0
−λ|x |β if x < 0
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Weighting and value functions
Risk aversion in the positivedomainRisk seeking in the negativedomain
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System 2 : Expected Utility Theory
For simplicity, we assume a risk-neutral DM.
The utility equals the outcome, i.e u(x) = x .
The expected utility equals the expected value (EV)
EU(x , p) = EV (p, x) = p · u(x) = p · x
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Mean difference in valence (drift rate)
System 1 – gain frame
δ1g = VG − VSg
System 1 – loss frameδ1g = VG − VSl
System 2δ2 = EV (G )− EV (S)
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For the predictions
Amount given: 25, 50, 75, 100Probability of keeping: 0.2, 0.4, 0.6, 0.8
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Predictions – Example 1
Parameters (PT from Tversky & Kahneman, 1992)
System 1 System 2
α = .88 no parametersβ = .88 to be estimatedλ = 2.25 δ2 = 0γ = .61
boundary: θattention time: t, E (T1)
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Predictions 1: Attention times
The size of the framing effect is a function of the time the DM operates inSystem 1. θ = 15; t1 = 0, 100, 500, ∞
Amount given25 50 75100 25 50 75100 25 50 75100 25 50 75100
Pr(
Gam
ble
)
0
0.1
0.3
0.5
0.7
0.9
1
Pr(keep)0.2 0.4 0.6 0.8
Loss frame
Gain frame
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Predictions 1: Attention times
θ = 15; t1 = 0, 100, 500, ∞
Amount given25 50 75100 25 50 75100 25 50 75100 25 50 75100
Pr(
Ga
mb
le)
0
0.1
0.3
0.5
0.7
0.9
1
Pr(keep)0.2 0.4 0.6 0.8
0
50
100
150
200
250
300
Mean R
T G
am
ble
0.2 0.4 0.6 0.8
Pr(keep)
25 50 75100 25 50 75100 25 50 75100 25 50 75100
Amount given
0
50
100
150
200
250
300
Mean R
T S
ure
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Predictions 2: Time limits
The size of the framing effect is a function of the time (limit) the DM hasfor making a choice. t1 = 100; θ = 10, 15, 20
25 50 75100 25 50 75100 25 50 75100 25 50 75100
Amount given
0
0.1
0.3
0.5
0.7
0.9
1
Pr(
Ga
mb
le)
0.2 0.4 0.6 0.8
Pr(keep)
Loss frame
Gain frame
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Predictions 2: Time limits
t1 = 100; θ = 10, 15, 20
25 50 75100 25 50 75100 25 50 75100 25 50 75100
Amount given
0
0.1
0.3
0.5
0.7
0.9
1
Pr(
Gam
ble
)
0.2 0.4 0.6 0.8
Pr(keep)
0
100
200
300
400
500
Mean R
T G
am
ble
0.2 0.4 0.6 0.8
Pr(keep)
25 50 75100 25 50 75100 25 50 75100 25 50 75100
Amount given
0
100
200
300
400
500
Mean R
T S
ure
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Modeling the drift rates – Example 2
System 1Preferences in System 1 follow PT.
System 2Preferences in System 2 are a weighted average of PT and EU.
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System 2: Weighted average of PT and EU
δ∗2 = w · (VG − VS) + (1− w) · (EV (G )− EV (S))
= w · δ1 + (1− w) · δ2.
Qualitative predictions remain as before.
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Modeling the drift rates – Example 3
System 1Preferences in System 1 are modeled according to a Motivationalfunction weighted by Willpower strength and Cognitive demand(MWC). (Loewenstein et al., 2015)
System 2Preferences in System 2 are modeled according to EU.
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System 1: MWC
Motivational function M(x , a); a captures the intensity of affectivemotivations
Function h(W , σ) reflects the willpower strength W and cognitivedemands σ.
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MWC
M(x , a) =∑
w(pi )v(xi , a)
w(p) is a probability-weighting function
w(p) = c + bp with w(0) = 1,w(1) = 1, and 0 < c < 1− b
v(x , a) is a value function that incorporates loss aversion
v(x , a) =
{a u(x) if x ≥ 0
aλ u(x) if x < 0
h(W , σ) is not specified but meant to be decreasing in W andincreasing in σ.
V(x) =∑
u(xi ) + h(W , σ) ·∑
w(pi )v(xi , a)
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Note
WMC: static-deterministic model
For predicting choice probabilities and choice responses time
→ dynamic-stochastic framework
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System 1 and System 2
MWC model assumes that both processes operate simultaneously.
Therefore, System 1 and System 2 merge into a single drift rate andthe two stages basically collapse into one single stochastic process.
With VG and VS indicating the subjective value of the gamble andthe sure option, respectively, the mean difference in valences (driftrates) in a gain and loss frame become
δg = VG − VSg
δl = VG − VSl ,
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Predictions
100
Amount given
75
50
p = 0.2
25 .1 .5
h(W,σ)
1 1.5
0.9
0.7
0.5
0.3
0.1
Pr(
Ga
mb
le)
100
Amount given
75
50
p = 0.4
25 .1 .5
h(W,σ)
1 1.5
0.9
0.7
0.5
0.3
0.1
Pr(
Ga
mb
le)
100
Amount given
75
50
p = 0.6
25 .1 .5
h(W,σ)
1 1.5
0.9
0.7
0.5
0.3
0.1
Pr(
Ga
mb
le)
100
Amount given
75
50
p = 0.8
25 .1 .5
h(W,σ)
1 1.5
0.9
0.7
0.5
0.3
0.1
Pr(
Ga
mb
le)
100
Amount given
75
50
p = 0.2
25 .1 .5
h(W,σ)
1 1.5
0.1
0.3
0.5
0.7
0.9
Pr(
Ga
mb
le)
100
Amount given
75
50
p = 0.4
25 .1 .5
h(W,σ)
1 1.5
0.1
0.3
0.5
0.7
0.9P
r(G
am
ble
)
100
Amount given
75
50
p = 0.6
25 .1 .5
h(W,σ)
1 1.5
0.1
0.3
0.5
0.7
0.9
Pr(
Ga
mb
le)
100
Amount given
75
50
p = 0.8
25 .1 .5
h(W,σ)
1 1.5
0.9
0.7
0.5
0.3
0.1
Pr(
Ga
mb
le)
100
Amount given
75
50
p = 0.2
25 .1 .5
h(W,σ)
1 1.5
300
200
100
0
Me
an
RT
Ga
mb
le
100
Amount given
75
50
p = 0.4
25 .1 .5
h(W,σ)
1 1.5
300
200
100
0
Me
an
RT
Ga
mb
le
100
Amount given
75
50
p = 0.6
25 .1 .5
h(W,σ)
1 1.5
300
200
100
0
Me
an
RT
Ga
mb
le100
Amount given
75
50
p = 0.8
25 .1 .5
h(W,σ)
1 1.5
300
200
100
0
Me
an
RT
Ga
mb
le
100
Amount given
75
50
p = 0.2
25 .1 .5
h(W,σ)
1 1.5
0
100
200
300
Me
an
RT
Ga
mb
le
100
Amount given
75
50
p = 0.4
25 .1 .5
h(W,σ)
1 1.5
300
200
100
0
Me
an
RT
Ga
mb
le
100
Amount given
75
50
p = 0.6
25 .1 .5
h(W,σ)
1 1.5
300
200
100
0
Me
an
RT
Ga
mb
le
100
Amount given
75
50
p = 0.8
25 .1 .5
h(W,σ)
1 1.5
300
200
100
0
Me
an
RT
Ga
mb
le
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Quantitative Model Evaluation
De Martino et al. (2006), Science
Guo, Trueblood, Diederich (2017), Psychological Science
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De Martino et al. 2006 behavioral data
Gain frame Loss framepro
b(G
am
ble
)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Prob(keep)0.2 0.4 0.6 0.8
pro
b(G
am
ble
)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Prob(keep)0.2 0.4 0.6 0.8
pro
b(G
am
ble
)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Initial amount25 50 75 100
pro
b(G
am
ble
)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Initial amount25 50 75 100
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Experiment
Guo et al. 2017
2 × (time limits: no, 1 sec) × 2 (frames: gain, loss)
72 gambles per condition, collapsed to 9 ”gambles” per condition
8 catch trials per condition
195 participants
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Models tested
Number ofModel parameters RMSEA
PTk 8 22,030PT with additional scaling factor 9 2,106Dual with PT and EU 10 932Dual with PT and weighted PT and EU 11 931MWCk 10 20,786MWC with additional scaling factor 11 3,177MWC2stages 12 2,578
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Model acccounts: Probabilities
Gainframe
PTDualMWCMWC2
Lossframe
no TP TPPr(keep)
0.27 0.42 0.57
Pr(
Gam
ble
)
0.25
0.5
0.75
Pr(keep) 0.27 0.42 0.57
Amount given32 56 79 32 56 79 32 56 79
Pr(
Gam
ble
)
0.25
0.5
0.75
Amount given32 56 79 32 56 79 32 56 79
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Model acccounts: RT – no TP
Gainframe
PTDualMWCMWC2
Lossframe
0.27 0.42 0.57
1.5
2
2.5
Pr(keep)
mean R
T G
am
ble
[sec]
Pr(keep) 0.27 0.42 0.57
me
an
RT
Su
re [
se
c]
1.5
2
2.5
Amount given32 56 79 32 56 79 32 56 79
me
an
RT
Ga
mb
le [
se
c]
1.5
2
2.5
Amount given32 56 79 32 56 79 32 56 79
me
an
RT
Su
re [
se
c]
1.5
2
2.5
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Model acccounts: RT – TP
Gainframe
PTDualMWCMWC2
Lossframe
Pr(keep) 0.27 0.42 0.57
me
an
RT
Ga
mb
le [
se
c]
0.4
0.5
0.6
Pr(keep) 0.27 0.42 0.57
me
an
RT
Su
re [
se
c]
0.4
0.5
0.6
Amount given32 56 79 32 56 79 32 56 79
me
an
RT
Ga
mb
le [
se
c]
0.4
0.5
0.6
Amount given32 56 79 32 56 79 32 56 79
me
an
RT
Su
re [
se
c]
0.4
0.5
0.6
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More details
Diederich, A., Trueblood, J. (2018) A Dynamic Dual Process Modelof Risky Decision-making, Psychological Review, 125, 2, 270 – 292,doi.org/10.1037/rev0000087
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Further directions
Risk attitudes
Gainframe
Lossframe
Risk averse Risk seeking
0
θS
θG
t
t1
System 1 System 2
P(t
)
0
θS
θG
t
t1
System 1 System 2
P(t
)0
θS
θG
t
t1
System 1 System 2
P(t
)
0
θS
θG
t
t1
System 1 System 2
P(t
)
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Further directions
Baron & Gurcay (2017) for moral judgmentsRT = b0 + b1AD + b2U + b3AD · U
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Further directions
0P(t
)
t
t1
t2
t3
1 > 0
2 < 0
1 > 0
2 > 0 Multi-stage decision model
Time schedule: deterministicor random
Order schedule:deterministic or random
Diederich & Oswald (2014) Frontiers in Human Neuroscience
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Further directions/Remark
Quantum Decision Theory (QDT), Favre et al. 2016 PlosOne
No claim that neurological processes are quantum in nature
Observables A = {A1,A2} and B = {B1,B2}Aj gambles; Bj confidence; (j = 1, 2)
Two states: decision-maker; prospect
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Further directions/Remark
Decision-maker state |ψ〉 ∈ HAB:
|ψ〉 = α11|A1B1〉+ α12|A1B2〉+ α21|A2B1〉+ α22|A2B2〉
with αmn ≡ αmn(t) ∈ CSuperposition state reflects indecision until choice is made
Time evolution related to endogenous (breathing, digestions, feeling,thought) and exogenous (interaction with surrounding) factors
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Further directions/Remark
Prospect state |τj〉 ∈ HAB:
|τj〉 = |Aj〉 ⊗ {γjj1|B1〉+ γjj2|B2〉}
= γjj1|AjB1 + γjj2|AjB2〉,
γjkl = 0, ∀j , k 6= j , l ∈ {1, 2}, γjkl ∈ C, j , k, l ∈ {1, 2}State reflects DMs indefinite mixed feelings about the setup andcontext choosing either lottery
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Further directions/Remark
Probability measure for choosing lottery Lj
p(Lj) :=|〈ψ|τj〉|2
|〈ψ|τ1〉|2 + |〈ψ|τ2〉|2
Compare to Luce choice rule
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Concluding remarks
Two-stage processes outperforms single processes.
Race (parallel processing): makes the notion of a faster System 1redundant (always the winner)
Mixture of two systems: makes the notion of two interactingprocesses operating together redundant.
QDT may be incorporated in framework.
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Thank you
Supported by
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