Policy Resistance Nc

8/6/2019 Policy Resistance Nc

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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

Not only BE and Nudging


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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=

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&area


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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


(*) 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


(*) 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


(*) 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


Network medicine (*)


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Network medicine ( )


(*) 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)


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)


Knowldege density biased


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Knowldege density biased


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


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


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


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


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


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


Policy Resistance Nc

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