miscellaneous factZ

Yesterday I attended the sixth and final of the series of “Workshops on Well-being” taking place at the LSE (I missed the fifth workshop as I was away and so the last one I attended was the fourth workshop back in April). This time the presentations were given by David Clark of KCL and Martin Knapp of LSE and KCL. Below are some heavily impressionistic notes.

Presentation by David Clark (KCL): Developing Effective Psychological Treatments for Common Mental Health Problems

1. Anxiety disorders

   • ~ 1/2 of mental health problems
   • overly pessimistic view on outcomes etc
   • can become obsessional (+ fear that thoughts are self-realizing)
   • If beliefs are inconsistent why do they persist
   • panic attacks (~ 30% have them once/v. occasionally but realize that they are not dying). But in the disorder people might have had them 5000 times — how can they still think they are dying when it happens again?

2. Research Strategy:

   • identify core cog. abnormality
   • …

3. Example: social phobia

   • most common anxiety disorder (lifetime prevalence: 12%)
   • persistent: natural recovery rate is 37% over 12 years
   • marked underachievement
   • persists because:
   • shift to internal focus (which means ignore external)
   • use of internal information to infer how one appears to others (and as they are anxious this unreliable)
   • safety behaviour
   • test some of this
   • Do high socially anxious individuals have an internal attentional bias (Mansell, Clark, Ehlers 2003)
   • Evidence that socially anxious individuals have a distorted external perspective (Hackmann, Surawy and Clark 1999)
   • Evidence that onset of phobia correlated some stressful (bad) social event
   • Does negative self-image affect relation with others. Yes, to some extent (another Clark paper)
   • treatment (Cognitive Therapy)
   • attention training
   • drop safety behaviours (to test no adverse consequences)
   • video feedback
   • rescripting early memories
   • does CT pass the randomized controlled trial: YES
   • compare against no treatment
   • placebo
   • at least as effective as medication

4. Common disorders where CBT is effective as a sole treatment (recovery rate, controlled effect size):

   • Major depressive disorder: 50%, -
   • Panic disorder 75%, 2.8
   • PTSD: 80%, 2.3/1.2
   • Social phobia: 75% 2.6
   • Generalized anxiety disorder: 50% (77%)
   • OCD: 45%, 1.5
   • Also show that effects of CBT persist for anxiety (unlike psychotropic interventions where there is a high relapse rate)
   • depression slightly different as naturally recurrent — though CBT still effective (and complementary to medication). Hollon et al (2005) (Arch Gen Psychiat) compare medication vs CBT over long-term and shows CBT better.

5. Evidence that benefits of CBT extend outside of targeted syndrome. Beneficial effects for:

   • other mental health problems
   • work, family, social adjustment
   • employment (less sick days, moving to work)
   • but these effect sizes are lower bound (overall want SWB scores …)

6. Developing more effective (shorter) treatments

   • Traditional approach is 1h/w for 3-4 months
   • but 1-2h of ‘homework’ per day between visits
   • Now trying intensive 1w course (~ as effective at least for PTSD)
   • Also treatments with extra-focus (e.g. social phobia + work: found big impact on time to get back to work)
   • CBT with well-being emphasis. Fava et al (2005) (Psychotherapy and Psychomatics). Find CBT-WB > CBT but tiny sample, no blind assessment etc.

7. Major policy changes underway to increase access to CBT

Martin Knapp (LSE + KCL): Economics of Mental Health: Some Open Research Questions

1. Why mental health is different

   • breadth/multiplicity of need
   • association with crime + violence
   • associated with suicide
   • compulsion, stigma, complex links with ethnicity

2. Leading policy/practice themes

   1. stigma/rights/social exclusion
   2. funding
   3. Balance of Care
   4. Treatments
   5. Prevention

3. Social exclusion, stigma, etc

   • Participation-based approach
   • opportunities, socio-economics roles
   • Rights-based approach
   • stigma, discrimination, compulsory treatment
   • If i were suffering from mental health problems I don’t want anyone to know (Scotland): 50% in 2002 to 41% in 2006 (following a big campaign)
   • evidence in UK actually may be getting worse (16% 2000 to 22% in 2007 on similar question)
   • Equity: great variations (inequality greater for mental health than for income), esp by ethnicity.
   • Costs:
   • total cost of depression £9 billion (Thomas and Morris, Brit J Psychiatry 2003)
     • mostly productivity effects (not service or morbidity)
     • prob. underestimate as also have staff turnover, presenteeism
   • major impact of psychosis on life-time development [ed: not exactly surprising …]
   • homicide: Taylor and Gunn (Brit J Psychiatry) show that across various European countries between 5 and 20% or homicides committed by those who are mentally ill

4. Funding

   • Mental health spend as %tage of total public spend: England is highest in EU [ed: is this good or just that England has a lot of mental health issues]
   • Good efficiency arguments for intervening (cost-effective)
   • Schizophrenia: total cost ~ £54k per person per year (only a 1/3 hits the health system)

5. Balance of Care

   • Massive reduction in number psychiatric beds (personal preferences, social preferences etc)

6. Treatments

   • Does it work?
   • Is it cost-effective? etc
   • In 2000 only 53% of people with depression received treatment compatible with NICE guidelines
   • More attention to non-health interventions
   • particularly risk factors such as bullying, family violence, uncontrolled debt

7. Prevention

   • Inner London Longitudinal Study (ILLS)
   • Study of all 10y old in part of London in 1970
   • Categorise into groups from: “no problems at school” to “conduct disorder”
   • Estimate costs to society per child from 10 to 28 (education, criminal justice, social services etc)
     • no problems: ~ 7k, conduct: ~ 24k, conduct disorder: ~70k (mostly criminal justice)
   • 1970 British Cohort Study
   • earnings at age 30 by childhood need at age 10
   • no probs: ~24k, behavioural (lowest quartile): same, Cognitive (lowest quartile): 15k, emotional (not a great effect but interacts in a minor way with cognitive). Another study finds same effects for behavioural at age 32 but extended to 48 finds same -ve effect of cognitive issues.

Over the last couple of months for the purpose of my research on happiness/subjective-well-being I’ve been putting together some notes on theories of contextual judgement. The first part of these is now in a form suitable for public consumption and I’ve posted them at:

http://www.rufuspollock.org/economics/notes/theories-of-contextual-judgement/

Following on from the third workshop a month ago, yesterday saw the third in the series of “Workshops on Well-being” take place at the LSE. This time the presentations were given by Mat White of Plymouth University and Andrew Steptoe of UCL. Below are some (very) impressionistic notes.

Presentation by Mat White (+ Paul Dolan): Accounting for the richness of our daily activities

1. Social psychologist: started out on risk perception, trust etc. (Fear of crime)

2. General problems with life satisfaction data

   • lots of it deals with attributes which are beyond realm of govt intervention (e.g. race, gender)
   • Response/cross person comparisons issue: same externals result in different reported happiness levels across individuals (e.g. old, poor people are happiest in Dolan’s Welsh data, perhaps because of a “Don’t grumble” attitude). [ed: essence is the qualia problem: can we compare different people’s report of their internal states, both across people and across time. Or more pithily: is my ‘Good’ or ‘OK’ the same as your ‘Good’ or ‘OK’?]
   • Subjective well-being isn’t one thing but a composite: SWB = Feelings + Thoughts + Time

3. Solutions

   • Experience Sampling Method: ask people during day
   • Problems: costly, only points in time, no duration etc
   • Day Reconstruction Method (DRM): solve duration issues
   • Can now base utility as integral of well-being function over time (ed: what utility always was but we just didn’t have the moment by data)
   • Find what one might expect re. what activities are nice
   • However no/v. weak correlation with e.g. income
     • But maybe because those payoffs are in the future
     • Or maybe because there are rewards in terms of thoughts, feelings about themselves etc (Eudamonia)

4. This project: add thoughts (about activities) to DRM

   • 625 Germans
   • 5815 Episodes (3057 single activities)
   • Online panel
   • Have 12 adjectives they can use which break down into ‘pleasurable’ and ‘rewarding’

5. Adding in Eudamonia makes a big difference!

   • Nice graph contrasting the DRM with ‘pleasure’ vs. ‘rewarding’ (at least partially inversely correlated).
   • Argue that we should sum both ‘eudaimonic’ and ‘hedonic’ evaluations over whole day.
   • Can now plot activities on x-y graph with x=hedonia, y=eudamonia (normalized about the mean values)
   • Get a slight -ve correlation
   • ed: this makes sense due to selection effects. Let w be total well-being and h hedonia score, e is eudamonia score. Suppose w is a linear combination of these underlying factors: w = h + k e. Now we would generally choose only to do activites with w > w0 (some outside option) => h+ke > w0 which gives the -ve correlation.
   • If reweight with duration [ed: equivalent to doing integral] then get a slight +ve correlation

   • ed: this reweighting by duration causes major changes to the form of the data. In particular all longer activities receive a positive shift while short ones receives a negative shift (explanation below). Whether this is what could/should do with the data was not entirely clear.

     Why does this shift occur. Results are plotted as ‘relative’ values (i.e. normalized about the mean). Thus if original value (x,y) it is plotted at (x-m1,y-m2) where m1 is the overall x-mean and m2 is overall y-mean. Adding duration means original values are now (dx,dy) and these are plotted relative to n1,n2 where n1,n2 are new duration weighted means.

     Letting dbar be the mean duration we could make the rough approximation that n1 = dbar m1, n2 = dbar y1. Then the new x position is: dx-n1 = dx - dbar m1 = d(x-m1) + (d-dbar)m1. Hence the new x-position will be a combination of a linear scaling out from the origin by d plus some offset of (d-dbar)m1. Since m1 is always positive this offset is positive (negative) as the duration of the activity is greater (less) than the mean duration of an activity.

6. After discussion

   • pop-ups (thoughts either +ve or -ve) have a big impact
   • in a regression on day-satisfaction number of +ve and-ve popups explained more than hedonic or eudamonic variables (total value for whole day)
   • could be useful to look at something more than a simple integral [ed: e.g. use contextual judgment stuff]
   • Eudamonia: enters day satisfaction regressions negatively. This is what we would expect given association of ’satisfaction’ with ‘pleasurable’ activites and slight negative correlation of ‘rewarding’ (eudamonic) activities with ‘pleasurable’ (hedonic) ones.
   • ed: could interpret eudamonic value as discounted future value coming from associated payoffs. I.e. if I work hard now this might not be pleasurable but it has high eudamonic content reflecting the future hedonic payoffs (nice garden, good holidays etc) of doing that work (NB: this is intentionally putting things very crudely).

Andrew Steptoe: DRM Analyses

1. Primarily interested in ‘positive affect’ and health outcomes

2. Questions:

   • how accurate is DRM
   • what does DRM tell us about activities and feelings of depressed people

3. Data: Daytracker study

   • 200 healthy women in full-time work
   • 2 x 24hr starting @ 5pm (one work day and one non work-day)
   • International dimension
   • EMA and DRM

4. Comparing EMA and DRM

   • Across aggregate data already see some differences (DRM shows noticeable rise towards end day while EMA does not really show this)
   • Per individual: similar differences but also fairly close correlation
   • Doing actual correlation looking at 4 different aspects (happy, tired, stress, anger) find medium correlations (0.2-0.7) which is reasonable but not great
   • also noticeable that timepoints are important: worst correlations are generally 12noon and 3pm

5. How accurate is the DRM for estimating feelings (esp. in relation to depression)

   • Do depressed people: have diminished pleasure in all activities or is reduced exposure to good stuff?
   • Depressed people are less happy across most interactions (except with Grandchildren) though effect (of depression) does vary and is strongest for being Alone or with your Partner
   • Looking at time: depressed people seem to spend more time (compared to non-depressed) doing things they don’t like
   • Similarly, looking across activities, depressed people are less happy doing most stuff
   • Again looking at time, seem to find depressed people spending more time on things that they particularly dislike (relative to others)
   • [ed: Not sure what this is telling us. After all the activities depressed people spend more time on may still be better than other options even if those options do not get as large a negative ‘hit’ from being depressed — e.g. house-work may not be much worse when depressed than non-depressed but it still might be worse than everything else]

Following on from the second workshop a month ago, today saw the third in the series of “Workshops on Well-being” take place at the LSE. This time the presentation was given by Andrew Clark of PSE. Below are some (very) impressionistic notes.

Presentation by Andrew Clark on Job Satisfaction: What do we Know and What Next?

1. Job satisfaction (JS) and individual well-being (LS)

   • well-being/LS function LS = f(Job satisfaction, health satisfaction, leisure etc)
   • data in BHPS (waves 6-15 though 11 missing)
   • health/ income / house / spouse / job / social life / amout leisure / use leisure (scale 1-7)
   • all highly significant
   • social life is top, followed by health, use of leisure, income and job satisfaction is last
   • robust to demographic controls
   • But do individual personality types make any difference (fixed effects)

2. Panel results

   • all effects go down (there are ‘happy types’) except JS (which doubles) though still the smallest
   • Is this ranking unique to Britain?
   • Is it the same for everyone? (subregressions: old/young, men/women; or do a latent class analysis)

3. JS is important to firms as well as it will predict worker behaviour

   • Labour turnover
   • Absenteeism
   • Counter-and non-productive work/productivity
   • Worker quitting (but almost impossible to do properly as quitting is self-reported so unreliable)
   • P(quit(t+1)) = f(JS, X(t))

4. Compare quitting GB and Germany

   • pretty similar, JS is pretty significant

5. Cognitive biases and context in relation to quitting (SPELL data from BHPS)

   • have panel data so can look at series of JS for an individual
   • refers to Kahnemann and Riedelmeier on evaluation of colonoscopy
   • suggest Peak-End evaluation: evaluations of peak and end point
   • Apply to job quitting (peak-end, min, max, avg, current …)
   • peak-end does best (followed by running max (close), and current)
   • => behaviour would not then seem to max their utility

6. Try do the same with income but need variations in income (since normally just rises)

   • use truckers as they have exog changes
   • other potential sources: tax changes
   • peak-end divorce

7. Relative income

   • Traditional: W/LS/JS = W(y,…)
   • Comparisons: LS/JS = W(y/yr, …)
   • yr is comparison/relative income
   • to whom do we compare? (peers, others in HH, spouse, myself in the past, friends, neighbours, work, expectations)
   • Results:
   • +ve effect of income
   • But falling as other’s income rises
   • Overall effect is zero: if everyone’s income rises then no effect

8. Preference for structure of income

   • same income but in different ways
   • flat slope (A), steeper (+ve) slope (B), and v. +ve slope (C)
   • even though flat slope (using saving to mimic C) would result in being overall better off
   • asked about this (they were told they could this) they still chose C (apparently because of self-control issues — they wouldn’t be able to save)

9. Does other’s income always affect one negatively

   • Hirschmann’s tunnel effect (happy for something good for you because it means something good is going to happen to me)
   • Danish ECHP (1994-2001): fantastic data (which gave not only individuals but all of their colleage’s info including pay)
   • here one does find a +ve effect of others income on me (check how it varies across firm so not just selection effect at firm level)

10. Do 2 wrongs make a right?

    • Peak-end utility could be thought of as ‘correct’ as:
    • with adaptation
    • current utility (after something good) understates actual total flow benefits (as one has adapted)
    • PE corrects for this

11. Instrumental uses of JS

    • ‘Good job’ lit has mainly focused on money
    • But self-employed earn less but are happier (though significat issues about reporting bias)
    • Also why are there different avg. wages in different industries (when they look the same)
    • Compensating differentials vs. rents
    • So let’s use JS to explain different
    • looking at the data: high wage goes with high JS (so suggests this about rents not compensation)

12. Job Quality: Are things going to the dogs?

    • ISSP (repeated XS in 3 waves 1989 - 2005)
    • Multivariate regressions: JS is improving (went down 1989-1997 but bounced back in 2005)
    • But stressful/dangerous/difficult work has been rising
    • Good job content has been going down.
    • However enough other stuff has been getting better faster (income, opportunities, flexible hours)

Discussion

• Paul Dolan:
  • Causality
  • Experienced Utility? Kahnemann would be unhappy
  • Peak-end seems difficult for JS since already a retro-spective evaluation (so peak-end of a peak-end)
• Gordan:
  • relative ranked position not just compared to the avg
  • care more about those above than those below
  • need to be more specific about form of relativities
• All: Context, Context, Context
• RP: Peak-end vs. range-frequency. Take colonoscopy: PE predicts that increasing pain at a single point (early on) would worsen evaluation while range-frequence would predict it would improve evaluation (since you spend more time at a level relatively better than the worst)
• BHPS: now have a question asking for whether your LS is better/worse than last year
• Gordan: gratitude is single biggest predictor of happiness
  • individual differences
• Propensities to adapt
• Gordon: Andrew Oswald and he also found +ve avg income effects in workplace
• Judgment vs. Adaptation
• Paul Dolan: generally we overestimate our +ve attributes but underestimate (their relative) income level

Originally status would have developed from some kind of of stimulus-response setup:

    Beating Competitor
            |
            V
      Higher Status
            |
            V            
Better Access to 'Resources'
  (e.g. Food and Partners)
            |
            V            
  Higher Survival Rate /
    More Progeny etc
            |
            V            
  Development of Reward System(s)
   for these outcomes (Food etc)   
            |
            |  (short-circuiting
            |   as with e.g. sex)
            |
            V         
Development of Reward Systems
 for Success in Competition 
     (Higher Status)

So status now has two components:

1. Increase in status improves access to ‘basic’ goods we derive direct ‘utility’ from (food etc)
2. Increase in status provides direct ‘utility’ independent of any impact upon access ‘basic’ goods.

What about respect? It could be argued that respect is a ‘basic’ good directly equivalent to type (ii) status. However I’m not really convinced of this for two reasons. First, ‘respect’ is fundamentally different from ‘normal’ goods in that one can select what you respect (and whose respect you care about). Second, and more importantly, as just outlined above, the desire for ‘respect’ or ’status’ seems to me a ’secondary’ desire, which has come about via a short-circuiting of our basic reward systems for ‘primary/basic’ goods.

Leaving this aside, the crucial point is that type (ii) status results in a pure zero-sum game. Thus, reducing competition for it (perhaps by increasing compassion) might move us to a (more) positive sum situation. Furthermore, the clear distinction between type (i) and type (ii) allow us to separate out ‘competing to survive’ (which might be essential) and ‘competing (just) to win’. This seems an important distinction to make. After all, we can all accept that, in a whole set of situations, successfully competing may be crucial to obtaining the basic resources needed to survive. However as we get wealthier it would seem that this first aspect diminishes in importance and the second (less healthy) aspect of status looms ever larger.

Just posted some early stage notes on experimental games in economics.

Following the first workshop a month ago, today I attended the second of a series of “Workshops on Well-being” at the LSE. Below are some (very) impressionistic notes.

Presentation by Paul Dolan and Robert Metcalfe: Valuing non-market Goods: Preference based and experience based methods

1. How do Value non-market goods?

   • Preferences
   • revealed (observed market behaviour)
   • stated (contingent hypothetical market)
   • Experiences: subjective well-being
   • Traditionally (implicitly) assume all of these are equivalent

2. urban renewal in swansea (from 2001)

   • 2 areas: hafod and landore
   • hafod has had renewal (500/950 homes)
   • landore (675) has not
   • compare the two (omitting those who have not yet had renewal and in-movers)

3. Improvements:

   • front boundary walls
   • street lighting
   • etc
   • landore: house cost 95k, income 16k
   • hafod

4. Estimate revealed preferences using land-registry data and dummy for renewal.

5. WTP: card they fill in saying what they are willing to pay for various improvements (per month for 3 years).

6. “Thinking about your life and personal circumstances, how satisfied are you with your life as a whole?”

7. 364 out of 1625 (22.4%) response rate (low but representative)

   • slightly biased to renewal area

8. Revealed prefs: no effect on prices of renewal (and apparently this had also been found in a much larger

9. WTP: £750 over 3 years (£250 a year)

10. 6.5% increase in life satisfaction (7.1 to 7.7 on a 10 point scale)

    • controlling for marital status, age, gender, income etc
    • approx 19k in monetary terms
    • but problems of endogeneity of income
    • instrument using partner in employment and rented accommodation well-being hit now is £6350
    • cost of renewal was ~14k

11. 2nd experiment: renewal in Port Talbot but not Neath

    • 8k for improvements + £250 for home safety per household
    • WTP: £500 (over 3 years)
    • SWB: 12.5k
    • but 2.5k for repair and 10k for safety!
    • income instrumented by WTP

12. What explains the discrepancy?

    • revealed preference may be wrong because of unobserved effects (e.g. improvements in Hafod).
    • WTP: evidence that per month/year figures would just go on forever (even though told just for 3 years).
    • SWB Income Compensation: might be discounted PV of long-term benefits.
    • WTP accumulate over 12-20 years (average time people are in the house) or discounted SWB IC over 25 years results in WTP value = SWB IC value.

Yesterday I attended the first of a series of “Workshops on Well-being” at the LSE organized by Paul Dolan, Richard Layard and Andrew Oswald. Below can be found some (very) impressionistic notes.

Talk by Andrew Oswald: Does Higher Job-Status Make a Person Healthier? A Longitudinal Test of the Whitehall Hypothesis

Basic (well-known) facts:

• Strong inverted u-shape in depression/anxiety over life-cycle peaking in mid-40s to mid-50s
• Need to move away from GDP to well-being in the next century
• More collaboration across discipline
• Across countries wealth correlated with happiness
• Within country across time (i.e. repeated cross-sections) no real growth in happiness (though growth in money)
• Now have data for Britain, Belgium and Netherlands and we reject null of no change in GHQ over time (so there is a decline in mental health over time).
• Can repeat across EU countries.

New social welfare functions:

• blood pressure, obesity, height.
• Life Satisfaction (LS) from NCDS: LS=f(high blood pressure, personal controls). high blood pressure enters negative.
• Well-being and height (guided by John Komlos)
  • danes and netherlands have been getting taller faster than anyone else (in US it may even be going down recently i.e. since ~1990)
  • this is interesting because danes and nthlands are happiest (and US is pretty unhappy)
  • height and happiness are correlated in rich EU countries (this still holds with deltas of height and happiness)
• Weight and well-being
  • BMI enters negatively in regressions for LS, Happiness, well-being (GHQ)
  • Christakis and Fowler The spread of obesity in a large social network over 32 years. NEJM 2007
  • Summarizes his paper on BMI with Powdthavee (where relative effects are significant)
  • u is concave in relative position then upward spirals, convexity then deviate from herd.
  • Relative BMI enters negatively (along with absolute BMI)
  • Carol Graham on US and Russians

Status and well-being.

• Look at the ‘Whitehall effect’ using longitudinal data. Contrary to existing cross-sectional results finding a robust effect they do not find such a result. This suggests that cross-section results may be picking up causality the other way and the resulting selection effect (people who are healthier get promoted).
• Redelmeier and Singh, Annals of Internal Medicine (Oscar winners live longer). But lot of issues statistically (not enough controls)
• Rablen and Oswald (2006), look at Nobel prize winners (vs. nominees). Get 1.6 extra years.
• Final aside: did experiment looking at reporting function on height. Found perceived height was linearly related to actual height.

Discussion

Paul Dolan

• When do we compare?
• Inequalities (higher moments …)
• Mental health and well-being (v. blurred distinction)
• International comparisons (are they useful)
• What measures if we move away from GDP?

Misc

• Range frequency theory
  • Parducci etc
• How happy they are with weight
  • Actual weight vs. actual weight
• Gilevich study
  • won a medal. Silver medallists less happy than bronze medallists.
• Increase in reports of morbidity but less actual illness.

Own questions

QU: if we’re hoping for a reorientation of public policy in relation to happiness and GDP) one would want to ask why hasn’t there already been a reorientation in relation to other areas (e.g. environment and GDP) — or at least why has it been so slow.

QU: Is relative effects in obesity coming from status stuff or from signalling (i.e. I use other people’s weight to compute what the optimal weight is). Has importance to determining policy impact.

Herewith are further (partial, impressionistic) notes from the second day of the two-day workshop (programme) on Rationality and Emotions organized by Miriam Teschl at Robinson College here in Cambridge.

S-Shaped Probability Weighting and Hyperbolic Preference Reversal - An Intimate Relationship by Herbert Walther

Walther has published these results as a 2003 paper in Journal of Economic Behaviour and Organization.

http://www.robinson.cam.ac.uk/academic/robinson_rationality_walther.pdf

Overview

• Empirical regularities:
  • hyperbolic discounting
  • sign effect: loss discounted less than gains
  • preference reversal:
  • magnitude effect: preference for losses before gains
  • s-shaped prob weighting (Gonzales and Wu 1999, Fehr-Duda, 2006 et al.)
    • prob weighting can explain Allais paradox
• How to resolve?
• Ans: EU maximizer considers anticipated emotions reactions to resoluton of uncertainty
  • prob weight derived via intertemporal state dependent EU max
  • using this can explain most empirical effects

Model and Results

Part 1: Generating the S-shape prob distbn

• EU of some binary prospect L(p, w1, w2), w1
• EU of wealth
• EU of elation (if you win)
• EU of disappointment (if you lose)
• last two both fade away over time

Part 2: Empirical regularities

Having generated the S-shaped result Walther goes on to show how this can generate most of the empirical regularities we are interested in.

• Look at some payment/contract whose probability of payment fulfilment is falling over time (this way we get probability in which we need)
• Now have some S-shaped setup and probability that goes into this S-shape is dropping over time (contract is less likely to be fulfilled).
• Hyperbolic discounting: can also now generate hyperbolic discounting within this same framework (other explanations e.g. Souzou 1998, Dasgupta and Maskin (2005) only do it on its own).
  • Logic underlying hyperbolicity: at start contract is very likely to be fulfilled so if it does not lots of disappointment — so (exp) discount rate is very high. Over time prob falls and S-shape prob distbn kicks in (so elation outweighing disappointment) and discount rate falls.
  • prediction: poor will show more hyperbolicity than rich
• The sign effect: gains discounted more than losses
  • Logic: again simple. If loss is very likely little disappointement but a very certain gain has lots of potential for disappointment.
  • prediction: again the sign effect is more pronounced for poor than the rich.
  • magnitude effect for losses: higher losses have higher impact that lower losses (because straightforward wealth utility becomes more important than disappointment/elation effect).
• preference reversal
  • poor will prefer losses before rich subject but gains after rich subject
  • preferences are same but marginal utility of wealth is different

Summary

• Simple model that is a small extension of basic EU maximization most of the empirical regularities.
• If diminishing marginal utility of wealth poor people will behave ‘less rationally’ than rich people despite having same preferences
• For the future: Why is prob weighting evolutionary sustainable?
  • potential answer: in hunter gather society there are externalities in that (large) gains and losses are shared (this would => S-shaped prob distbn).

“It’s a boy! Behavioural and Neural Evidence on Self Delusion” by Danica Mijovic-Prelec

• Deficits (due to lesion) on right side of brain lead to deficits in left hemisphere
• Furthermore these patients are not aware of the deficit and deny its existence (to the extent of confabulating experiences)
• Sackheim-Gur 1979: self-deception in social psychology
  • played people mixture of their own phone and others
  • averse to your own voice
  • people would not hear their own voice and furthermore physiological measure of stress indicated it went up when ‘not hearing their voice (when it was there) — i.e. when people were self-deceiving
• Sackheim-Gur criteria:
  • individual holds 2 contradictory beliefs
  • beliefs held simultaneously
  • individual is not aware of of holding one of the two beliefs
  • nonawareness of this belief is motivated

Experiment

• shown korean figures and asked to classify as male/female
• first stage: get figures and must classify (5c for each correct prediction — correct measured against classification by some control group)
• second stage: must also predict gender of next figure (and then classify)
  • paid like before for classification but bonus for being in top x% of predictors

Results

• Focus on items that were ‘well-classified’ by control
• First classification: 65% accurate
• Anticipation: 50% accurate (as expected since randomized)
• Second classification: <65%
  • anticipation effects classification
• stronger for males: anticipated as male results in classification as male 72% (for females like first time round)
• calculate self-delusion index for each subject
  • four options for response pattern (starting with female) of form 1st classif, anticipation, 2nd classif:
    • FMF: honest
    • FMM: self-deluding
    • FFM: inconsistent
    • FFF: consistent
  • need to subtract inconsistency from self-delusion percentage to get ‘true’ self-delusion
  • index = % self-delusion / % inconsistency (could use difference)
• fMRI
  • expect that self-deluding subjects behave differently from inconsistent (and honest and consistent)
    • notably don’t show this activation on consistent trial (when they also confirm their prediction)
  • this is what they find (v. significantly)
  • in attentional and cog. control regions
    • self-deluding and inconsistent is similar
    • however big difference in parahippocampal gyrus (associated with memory)
• [rp] question: could some of this come from a priming effect combined with better recall. I anticipate X, which primes me. Then suppose I see the figure and have a vague recall from before. Suppose that people experience different priming effects — then those with a strong priming effect feel conflicted and have more stress (i remember Y sort of but do I really or I just doubtful because of having seen X) which means more fMRI anomalies and and means they are more likely to ’self-delude’ while those with weak priming simply aren’t sure what they think (not really excited/conflicted) and just go randomly with M/F (so ’self-delusion’ or ‘inconsistency’ are equally likely).

Herding and Social Influence in Economic Decision Making by Michelle Baddeley

• Solomon Asch
  • Length of line experiments (everyone says line is B when actually A)
• Task design: stock-picking
  • two charts for past prices of a stock
  • shown faces along with their associated choice (controlled by experimenter)
• Results:
  • strong effect of other decisions on own decision (on average 72% vs. 50% choose the one chosen by herd)
  • perhaps not very surprising here given the lack of info about stocks (and their underlying equivalence) — a small piece of information should have a dramatic effect

Today I attended the first day of a two-day workshop (programme) on Rationality and Emotions organized by Miriam Teschl at Robinson College here at Cambridge. The mix of economics, psychology and neuroscience has so far been fascinating and below I include some general ‘impressionistic’ notes from some of the sessions so far.

Stress and Euphoria on a Trading Floor by John Coates

Overview

• Better title might have been ‘Fear and Greed’.
• Managed a trading desk on Wall Street.
• Are fear and greed exaggerated by a steroid induced shift in risk preferences?
• Cortisol rises in a market crash, raises risk aversion and accentuates crash.
• Little literature on hormones
  • one paper on oxycytocin and trust
• Steroids have very widespread effects in body (everything from body shape to cognitive function).
• Cortisol follows adrenalin as a stress response.
  • Acute exposure: euphorogenic, increases motivation (+ve basically)
  • Chronic exposure: bad. Selective attention to negative precedents (Erikson 2003) etc.
    • Decrease risk preferences
• Traders
  • 17 males, 19-38 from City Trading Floor. Healthy and no outside source of stress.
  • No overnight positions, no salary, no bonus. Just given capital and trade (each with own deal: basically a percentage or profits)
  • Annual P&L range: -10k to +5m
  • Experiment was live because you could not replicate high stakes trading in the lab.
  • Trading US Bond Futures adn Bund Future, Dax, and Euro/$
  • German market main component of P+L so that was the focus
  • Use calendar of econ stat announcements as ’stress’ events (some traders make almost all their money just when US employment info comes out)
• Data:
  • Sampled 2x a day (11am,, 4pm) to bracket NY 0830 news releases
  • P+L at 11am and 4pm
  • Long term P+L
  • Plus other info

Results

• Massive volatility in cortisol: cortisol should drop by 30% in a normal subjects but in 40% of subjects upwards slopes were increasing by 500%
• No relation of cortisol to P+L at all (surprising: but this was not a a very volatile period).
• But a strong relation of cortisol and std dev of P+L
• Next look at effect of expected volatility (measured by implied volatility measures used to price derivatives) to see whether this affects cortisol release
  • incredibly good fit (people were excited because volatility means there is money to be made)
• Conclusion: only saw acute exposure effects (so mainly excitement)
  • prolonged exposure could be very different: ‘irrational pessimism’ (but no evidence in this paper)

The Psychology of Gambling Behaviour by Dr Luke Clark

Overview

• Lots of Gambling and it has been increasing (9.6bn a year)
• Structural characteristics lead to overestimation chances of winning
  • Erroneous verbalizations using ‘think aloud’ (Gaboury and Ladouceur 1988) — up to 80% of their thoughts are irrational (problem gamblers show more)
  • Gamblers develop ‘illusion of control’ (Langer 1975)
  • Failure appreciate independence of turns (Wagenaar)
• Investigate particular items
  • Effect of ‘near-misses’: people play more when more near-misses (Kassinove and Scharre 2001 find maximum at 30% of near-misses)
  • Effect of personal control:
    • craps: people bet more on their own throw than on others’ throws
    • roulette: higher bets when palyer versus croupier throws ball
    • lottery: players demand more to exchange self-selected tickets ($9) than lucky dip tickets ($2) (Langer 1975)
• Experimental design like a slot/fruit machine (but with participant having chance to select value on left ‘wheel’)
  • Thus can investigate both control and near-misses at same time
  • Also elicit feeling at particular points of time

Results

• Behaviour
  • control results in significant increase in ‘happiness with situation’
  • happier when they win if ‘in control’
  • interaction of control and near-miss: when in control near-miss increases desire to continue (but this is not so when computer ‘in control’)
• Arousal:
  • near-miss impact on arousal depends on control (w/o control no response)
• fMRI
  • participants who show more erroneous beliefs in questionnaire show more activity in response to near-misses

Summary

• Gambling-related cognitive distortions can be elicited on a laboratory task
• Personal control increased perceived chance of winning and pleasure at winning
• Near-misses are aversive but encourage continued play (*when player ‘in control’)

Time and Emotions by Stephane Luchini

Overview

• People prefer to ‘consume’ something unpleasant before something ‘pleasant’ (if they have a choice)
  • seems problematic as with discounting would expect the opposite
• One explanation: people ‘consume’ an event both at the time it happens and before (thus want the good thing second so you ‘anticipate’ it for longer).
• But what about the effect of the anticipated outcome on the subjective experience of time
  • Significant amount of evidence that emotions affect time perception
• Emotions are generated by differential/passage between two situations (not time dependent)
• Anticipated duration is function of actual clock time and the basic emotion which depends on difference between current state and future state.

Results

• To obtain time reversal (negative time preference) emotions must have strong impact on time discounting
• Only can occur if the the future date is not too remote.