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Coping with policy resistance in acomplex world: not simply BENU
Cristiano CodagnoneIPTS Behavioural Economics Seminar Series
Session 4
Seville, 9 June 2011
To guide the motions of the human puppet, it is necessary to
know the wires by which he is moved
M. Helvetius, 1810, A treatise on Man, his intellectual faculties and his education
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Explaining the title
Policy resistance : tendency for interventionsto be defeated by the systems response to
the intervention itself (*)
Main causes: a) intrinsic complexity (*); b)inadequate policy making intellectual framework
(*), (**); c ) ineffective use of, often, weakevidence (***)
For over sixty years generations of policymakers have
been raised to have a mechanistic view of the world, anda checklist mentality: to achieve a particular set of aims,
draw up a list of policies and simply tick them off (**)
(*) Sterman, J.D., Learning from evidence in a complex world. Am J Public Health, 2006. 96(3): p. 505-14
(**) Ormerod, P. N Squared: Public Policy and the Power of Networks, RSA 21st Enlightenment
(***) Ongoing work for forthcoming monograph on impact evaluation in the domain of Information Society
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Contents
Part I Three Disclaimers
Part II Obesity as anchoring in a social network
Part III Privacy: survey results and BE concepts
Part IV ICT for better health
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Part I: 3 disclaimers
Syncretism
Questions rather than answers
Criticism
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syncretism: melding eclectic thoughts
foundationalism vspragmatism in evidence
based policy-making
Ontological certainty vsontological unpredictability
?Complexity andsystem thinking
Behaviouraleconomics and
nudging
Innovativemodelling and
the data deluge
Networkanalysis
Obesity,smoking and
binge drinking as
anchoring innetworks
Understanding
privacy throughbehavioral
hypotheses andsurvey data
Envisaging thefuture: ICT meetsscience toward
better health
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castles in the air or truth to power?Reforms as experiments in an opensociety (Popperian Inspiration):
Initial foundationalism with its claimon privileged knowledge based onmethodological strategic choices
Pragmatism
Ideas become true just in so far asthey help us to get into satisfactoryrelations with other parts of our
experience (William James,Pragmatism: A New Name for some OldWays of Thinking, 1907
Pragmatism in policy evaluation
User friendly and user oriented
aggregation of facts using withcraftsmanship all available tools
Policy makers and politicians do not reador read at most 11 minutes a day, theyform their ideas in exchanges withlobbyists, think tanks, and consultants,
so lets give them a few factoids for thepress release: eHealth to potentiallyincrease GDP by X%
2.6B
3.5B
1.4B
EVIDENCE USABILITY
EVI
D
EN
CEV
A
LID
ITY/
RELIA
BIL
ITY
LOW
HIGH
LOW HIGH
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Spotting the blind spots
Beobachter [...sind] blind fr ihren blinden Fleck
Niklas Luhman
Literal translation
The observer is blind for his blind spot
Or a different way to look at it:
The system does not see what it does not see
Asking the right questions instead of providing one fit all answers canhelp spotting the blind spots of policy making
Including disentangling what is fad and fashion about BENU from how itcan contribute, among all other approaches and instruments, to improveevidence use in policy making
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Critical frame of mindBut, if constructing the future and settling everything for all times are not our affair, it is
all the more clear what we have to accomplish at present: I am referring to ruthlesscriticism of all that exists, ruthless both in the sense of not being afraid of the results it
arrives at and in the sense of being just as little afraid of conflict with the powers that beKarl Marx , Letter to Arnold Ruge, September 1843
(first published in Deutsch-Franzosische Jahrbucher, February 1844)
Big Society and Nudging a nice wayfor the Cameron Cabinet to say small
state and distract public attention
from spending cut?
The we know better:
First came the Jacobins
Then the Bolshevik
And now the Nudgers?
Who nudge the nudger? Policy makersalso prone to same biases and not
necessarily motivated by maximization
of the public good
What is new about BE?
David Hume: we favour the present over the future
Adam Smith: disproportionate aversion to loss
Karl Marx: False consciousnessBE not really a revolution:
So different from Game Theory?
Still agents maximising a preferencerelation over some space of
consequences and the solution inmost cases still involves standard
equilibrium concepts
BE intrinsic limits:
Only the individual, groups&institutions?
Anchoring from the Framingham HeartStudy (3 cohorts over 31 years) versusexperiments mostly with students, who
know they are observed, have small stakes,
etc
POLICY RESISTANCE
INADEQUATE FRAMEWORK
INEFFECTIVE US OF EVIDENCE
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Part II: Obesity as anchoring
Some background on obesity
Framingham Heart Study data analysis
With a little help from my friend(*)? N Square
(*) Using headline of New York Time article on obesity and network ( 18 September 2010)
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A Tsunami in 3 decades
( + 125.3 % )
( + 250.0% )
( + 85.5% )
( + 151.5% )
( + 131.4% )
( + 61.9% )
( + 41.4% )
( + 39.1% )
( + 93.1% )
( 137.9% )
( 45.9% )
OECD Health Data( * ) data comes from measurement, all other are self-reported
Not predicted nor prevented a Tsunami of poor health, entirely mediated bymodernised lifestyles, has hit our shores on both sides of the Atlantic
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Modernity is outpacing biology
5-6 Million Years 3-4 Decades
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Determinants of obesity: standard view
Thrifty Genotypes/Phenotypes
Dietary Input
+ Energy intake
+ Glycemic indices+ Ingestion Frequency
-- Dietary Fibers Lipid profile
Energy Output
- Transportation costs- Food preparation /subsistence costs
- Micro environmental
thermal costs- Leisure activity costs
- Work/ occupation costs
Adverse outcomes
Diabetes IISyndrome X
ObesityHypertension
HyperlipidemiasHyperinsulinemia
CDV diseases
Environmental factors Food relative prices
Retail /logistic (fresh food) Image of, exposure to, food
Information, educationBuild environment
Family structure TV and digital leisureHealthcare delivery
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The Framingham Heart Study: basic information (*)
Interconnected social network of 12,067 people, assessed repeatedly from1971 till 2003:
1948 5029 enrolled
1971: most of the children of original cohorts and their spouses enrolled
2002: third generation enrolled
All sort of network related information available and analysed: Ego: person whose behaviour is analysed
Alter: A person connected to the Ego who may influence him/her
Degree of separation: social distance between two people measured by the
smallest # of intermediaries between an ego and others in the network
(*) Christakis, N.A. and J.H. Fowler, The spread of obesity in a large social network over 32 years. N Engl J Med,2007. 357(4): p. 370-9
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Visualisation
http://www.nejm.org/action/showMediaPlayer?doi=10.1056%2FNEJMsa066082&aid=NEJMsa066082_attach_1&area=
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http://www.nejm.org/action/showmediaplayer?doi=10.1056/nejmsa066082&aid=nejmsa066082_attach_1&areahttp://www.nejm.org/action/showmediaplayer?doi=10.1056/nejmsa066082&aid=nejmsa066082_attach_1&areahttp://www.nejm.org/action/showmediaplayer?doi=10.1056/nejmsa066082&aid=nejmsa066082_attach_1&areahttp://www.nejm.org/action/showmediaplayer?doi=10.1056/nejmsa066082&aid=nejmsa066082_attach_1&area8/6/2019 Policy Resistance Nc
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Findings
Directional influence in friendship ties If an ego stated that an alter was his/her friend, the egos chances of
becoming obese appeared to increase by 57%, if the alter became obese
Between mutual friends the egos risk of obesity increased 171% if an
alter became obese No statistically significant relation when the friendship was perceived by
the alter but not the ego
Degree of separation, quality of tie, and geographical distance Among pairs of adult siblings: increased chance by 40%
Risk that a friend of a friend of an obese person would be obese was20% higher than in a random network (10% at 3 degree of separation)
Whereas degree of separation decreased the effect of an alter on anego, geographic distance did not
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Discussion
Authors conclude they have compellingly shown that obesity may spread insocial networks in ways shaped by the nature of social ties
Assuming data have been correctly tortured, why is it so?
Alter directly influences egos behaviour Alter changes egos norms of acceptability:
Special case of anchoring to your alter as implicit reference point
So as increased BMI spread our anchor may also increase
Other interesting implication
Assume that for any X people who stop smoking, there will be an additionalpercentage of X that eventually will quit as well(*)
Assume now you run a randomised control trial using mainstream HTA to
evaluate a lifestyle intervention helping X people to stop The cost per QALY measure from the above will underestimate the actual net
effect of the intervention
The more so if among the X you catch one who is an influential alter for manyegos
(*) Christakis, N.A. and J.H. Fowler, The collective dynamics of smoking in a large social network. N Engl J Med,2008. 358(21): p. 2249-58
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Binge drinking in the UK
Ormerod P and Wiltshire G, Binge drinking the UK: a social network phenomenon. Mind and Society2009. 8(1): p. 135152
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N Square: Nudging plus network (*)
(*) Ormerod, P. N Squared: Public Policy and the Power of Networks, RSA 21st Enlightenment. Quoting D Watts.A simple model of global cascades on random networks,in Proceedings of the National Academy of Science,99, 5766 5771, 2002.
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Different types of networks
Random network
Scale free network: few nodes have many links (tipping points,influencers, etc)
Small world
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Networks effect and policy
Nudge a few to obtain success
But very complex If the world is like: increased uncertainty
Unintended consequences and resistance
What worked? The treatment or the network?
Ormerod syncretic approach: Traditional survey information on network
Agent Based Modelling Simulation
Nudging focal nodes in the networkNot only BE and Nudging
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Part III: Privacy
Industry knows it better Selective survey results
Typical case for prospect theory Information ineffective
Privacy 2.0: ownership and PDV
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Floating data for price discrimination (*)
We leave traces of our behaviour everywhere that are more or less legallyaccessible:
Online shopping details
Catalogue purchases,
Magazine subscriptions,
Leisure activities
Information from social-networking sites
Health Insurers are testing modelling and data mining technique to :
Identify customers risk profiles
Segment their offer and/or select their customers (avoiding adverseselection through price discrimination)
All of the above, if it works, at substantially lower costs and in muchshorter time if compared to traditional methods
(*) Insurers Test Data Profiles to Identify Risky Clients Wall Street Journal, 19 November 2010
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marketing data to predict life spans (*)
(*) Insurers Test Data Profiles to Identify Risky Clients Wall Street Journal, 19 November 2010
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Selective results from survey (*)
For instance Some feel entitled to protection of information about themselves and then end up
trading away that same information for little reward
People change idea on whom to grant access to their credit card transactionaldata, if they are asked about repeatedly
Feeling their privacy protected by merchants offering SSL connections (security orprivacy)
Presence of privacy policy taken by many to mean protection regardless of content
Privacy seal interpreted as guarantee of trustworthy website
Even well presented warning about spyware not always leads individuals to abort
installations
Users of social sites do not change their default settings
Rapresentativeness heuristics : neat appearance of website=trustworthiness
Users think that providing personal information on social networks could causeprivacy problems to other users but not to them
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(*) Most reported in Acquisti, A., & Grossklags, J. (2008). What can behavioral economics teach us about
privacy? In Digital Privacy: Theory, Technologies, and Practices (pp. 363-377). New York and London: AuerbachPublications; some confirmed by Eurobarometer Survey on eID (Joint IPTS/DG INFSO, DG Justice project)
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Why? objective difficulties
Data subjects know less than data holders about magnitude of datacollection and possible secondary usage: become subject to externalities
Complex life-cycle of information in digital society has multitude ofconsequences individuals can hardly imagine
Two unknowns: what privacy-relevant outcomes, with what consequences
Even when aware about risks, they miscalculate probability and magnitude ofoccurrence
Privacy risks by-product : privacy good attached to other goods in a bundle
Privacy costs and benefits difficult to estimate, they are immaterial: ambiguity
about probability and value of outcomes
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Why? Cognitive biases
Even with full information and cognitive power to process it, we fail to do sodue behavioural anomalies and biases:
No consistent preferences between alternatives
Anchor value of personal information to arbitrary value: difficult to set a price,but once one accepts reward offered by merchant that may become the anchor
Tend to discount as improbable an identity theft
Valence effect: overestimation of the likelihood of favourable effects.
Rational ignorance: cost of learning higher than potential benefits
Status quo bias: default privacy settings
Who can ex post, and even the more so ex ante, assess the costs and benefitsof privacy decision making based on simplified heuristics?
Behavioural economics offer a number of tools of better understanding privacydecision making
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Limits of information (*)
Commission find it easier to go for information requirements rather than forharder interventions:
i.e. Directives 2002/65 on distance marketing of consumer financial services(OJ 2002 L 271/16) imposes obligation to provide consumer with information on
some 40 pieces of information
A win-win approach for 2 out of 3 (output in place of outcome game)
Policy makers tick their boxes,
Traders are often happy to cover their backs by over-supplying information Consumers, limited by the magic number 7, are forced into rational ignorance
Conclusion
Information ok
Nudging also good (change default values: cooling off periods, etc)
But they cannot substitute liabilities, bans, etc
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(*) Howells, G. , The Potential and Limits of Consumer Empowerment byInformation. Journal of Law and Society, 2005. 32 (3): p. 349-70
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Privacy 2.0 (*)
Codes of Fair Information Practice:
data privacy practices by government and corporations
no longer adequate: distributed data collection, data mining, and easydissemination
Personal participatory sensing: sensing devices of all kinds deployed byindividuals change expected flows of information of both pubic and private
spaces
Community-Based Participatory Research (CBPR):
Success in health and environmental research: validity of data marginalised groups to act on the data they helped collect and analyse
Users see data as theirs, care about them, change attitude: ownership
Personal Data Vaults (Google Health, Microsoft Health Vault)
Owner is the user Displace ineffective policy efforts guided by wrong intellectual framework ( i.e.
on electronic identity, inter-operable personal electronic health records) and thelikes
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(*) K. Shilton et al, 2009, Designing the Personal Data Stream:Enabling Participatory Privacy in Mobile Personal Sensing
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Part IV: The future: ICT for better health
Barabasi: advent of network medicine The view of the clinician
Crowds, modelling, persuasive technologie
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Network medicine (*)
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Network medicine ( )
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(*) A. Barabasi, Network Medicine From Obesity to the Diseasome, N Engl J Med, 357, 4: 404-407
Example from COPD (*)
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Example from COPD ( )
Courtesy of Prof Josep Roca (from his presentation Predictive Medicine: the view of the clinician)
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More knowledge needed (*)
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More knowledge needed ( )
Courtesy of Prof Josep Roca (from his presentation Predictive Medicine: the view of the clinician)
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Knowldege density biased
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Knowldege density biased
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Predic
tiveCapac
ity
Low
Little/ no information
Zone
Illness ZoneWellness
Authors visual essay
Power law distribution
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Power law distribution
Immagine nodes are health data and links are their co-occurrence with otherhealth data for each of the N=individuals
And immagine a third (N=individuals in good or poor health) ax
We may verify that 20% of key health data have 80% of co-occurencesexplaining poor or good health!Not only BE and Nudging
Crowdsourcing, living epidemiology, and persuasive technology
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g, g p gy, p gy
Community based crowdsourcing: high yield
health data Individuals input a few high yield data (nutrition,
activity and key physiological parameters) forthemselves and their children
They use ubiquitous mobile technologies tocommunicate and receive data
They are provided with new functionalities todetect and input physiological parameters
Living epidemiology: real time analysis of co-occurrences Researchers (granted access to the data) identify
the per unit and crowds co-occurrencesexplaining wellness or illness
Evidence used to evaluate and support publichealth investments
Evidence mapped against characteristic ofcommunity help shape other interventions
Persuasive technologies: change behaviour Can integrate classical interventions
Can overcome and avoid the resistance thatthese interventions encounter
It is a quintessential nudging approach leveraging
crowds participation and network effect
Crow dsourcing
of simplebut High YieldHealth
Data
data
intensivepredictive
modelling
Personalized
feedbackand nudges
throughpersuasive technology
Prediction &
Prevention
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Nudging to avoid hard battles?
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Nudging to avoid hard battles?
OBESITY
Impose Taxes(on individuals, on junk food)
Menu labeling in fast food
Alter relative prices(agricultural policy)
More effective and spreadfood labeling
No vending machines inschools
Restriction in advertising ofnutrition poor food
Information / educationalcampaign
Intervention on the builtenvironment
Integrated health and socialcare prevention
New battlelike with
Tobacco? Individual responsibility(you get what you deserve)
NEITHER
State Big Brother(I care and choose for you)
NOR
Libertarian paternalism(architect/ steer choices)
BUT
Industry script in the US is clearAmericans need to be more activeand take greater responsibility fortheir diets M. Kent, Coca-Colas CEO
(Wall Street Journal Oct 7 2009, op-ed in responseto envisaged taxes on soft drinks)
The bad guys
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Conclusive thoughts
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Back to the future
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Science paradigms
Past 3000 years: Empirical
Past 400 years: Theoretical-analytical
Past 30 years: Computational
Tomorrow: Data intensive eScience
ac to t e utu e
The Greek provided the universal analytical proof that the square on the longside of every right angle triangle has the same area as the sum of squares onthe other two sides
Babylonian engineers simply measured the sides of a thousand right triangularand heuristically induced the same conclusions
Needle in the haystack: benign andmalign co-occurrences explaininggood or bad health
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Final considerations
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One, certainly useful, thing is BE and its experiments
Another one is integrating BE with other insights and tools to:
Reinforce the evidence base for policy making
Help drive a paradigm shift in policy making intellectual framework
Spotting and correcting blind spot
Refining evidence and approach can lead us closer to the never endingprocess of approximation to know the world
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At the end of the day, however
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y,
what counts as evidence or what counts as research involvenot just technical objective judgments but also subjective and
contextualised assessments. The attaching of labels such asevidence or research to particular types of knowledge are infact a political actWe need to recognise then that knowledge,evidence and research are all privileged terms that reflect theperceptions, priorities and power of those who use them
(Polanyi, 1967; Foucault 1977; Giddens, 1987). Thus the playingout at ground level of debates about what counts as research isby no means always a rational/technical matter, but insteadinvolves a complex deployment of both technical expertise and
power dressed up in the guise of rationalityS Nutley, I Walter and H. Davis, Using evidence: how research can inform publicservice, Bristol, The Polity Press, 2007, p. 25
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Thanks for your attention
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How It may works
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Institutionallegacy data
communitydata commons
High yieldHealth data
Evidence basedchoice architectures
& public healthpolicies
Visualisedeffects of publichealth policies
change
Personalisedco-occurences +Network based
nudges
Visualisedeffects of benign
& malignatCo-occurences
?
Visual analyticsand simulation
Complex
modelling
Share
dsema
nticd
ata&distribu
ted
computi
nginfra
struc
ture
Visual analyticsand simulation
Ubiquitous mobilemedia andsensors
Worldwide scientific andpractitioners community
Social networkanalysis
+
Persuasivetechnologies andserious gaming
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