1 Partnership in regulation: Making the case for Smarter Intervention Hanne Melin eBay Inc. Public Policy Lab 2 INTRODUCTION “Smarter Intervention” is a methodology for better decision making in public policy, developed by Mark Fell and championed by eBay Inc. Public Policy Lab. It is a natural next step for the European Commission’s better lawmaking agenda: First we had Better Regulation, focused on improving the quality of policymaking. Currently we have Smart Regulation, focused on improving the quality throughout the policy cycle. Now it’s time for Smarter Intervention with a focus on improving the quality of our very policy responses. Something which depends on partnership in regulation. This report illustrates why we need partnership in regulation, what it entails and how it could look
Transcript
1
Partnership in regulation: Making the case for Smarter Intervention
Hanne Melin eBay Inc. Public Policy Lab
2
INTRODUCTION “Smarter Intervention” is a methodology for better decision making in public policy, developed by Mark Fell and championed by eBay Inc. Public Policy Lab. It is a natural next step for the European Commission’s better lawmaking agenda: First we had Better Regulation, focused on improving the quality of policymaking. Currently we have Smart Regulation, focused on improving the quality throughout the policy cycle. Now it’s time for Smarter Intervention with a focus on improving the quality of our very policy responses. Something which depends on partnership in regulation. This report illustrates why we need partnership in regulation, what it entails and how it could look
The Orbit tower was ready for the London Olympic Games 2012. Its unstable form reflects today’s social complexity and great flux.
… technology forges interdependencies … … interconnectedness … … and it underpins almost everything. Data analytics even become the raw material for art, such as the work by Jer Thorp showing how often the New York Times printed the words “hope” and “crisis” between 1981 and 2010.
18 – 1. DEFINITION AND DRIVERS OF FUTURE GLOBAL SHOCKS
Centralisation and concentration of systemsConcentration, if not centralisation, has become an important facet of efficiency for
transportation hubs and financial payments. As a network structure, a hub allows greater flexibility within the transport system and transaction speed within the financial payment system. If a major hub is disrupted, however, delays may ripple through interconnected supply chains. This not only upsets the functioning of the tightly knit transportation and financial payment sectors, it induces volatility that may lead to losses in productivity, for-eign investment and access to exports, whether they be food, water, electricity, productive capital or some other scarce resource. Part of the challenge in preparing for and manag-ing the risk of future global shocks is to diversify these hubs or to build-in greater system robustness and redundancy.
For example, there are four major air freight carriers that account for the bulk of global air cargo. Each has a hub-and-spoke organisation of their network with hubs clustered around the world’s three major zones of economic activity; North America, Europe and Pacific Asia. The choice of the main consolidation hub is based upon an airport that is well located, has good infrastructure, but that does not service a very large local passenger market to ensure it is the airport’s main customer and receives privileged access to the run-ways. There is a high level of concentration of hubs in the Eastern part of the United States, which roughly corresponds to its demographic concentration. Disruptions to this hub result in bottlenecks and delivery delays to the rest of the continent.
Source: NARUC (The National Association of Regulatory Utility Commissioners) (2005), Technical Assistance Brief on Critical Infrastructure Protection, “Utility and Network Interdependencies: What State Regulators Need to Know”, US, April, available at www.naruc.org/Publications/CIP_Interdependencies_2.pdf, p. 8.
Source: Thurner, S. (2010), “Systemic financial risk: agent-based models to understand the leverage cycle on national scales and its consequences”, OECD, Paris.
Figure 2.4. Network analysis: a diagram of systemic interbank exposures
.
.
.
Bank 1
Bank 2
Bank 3
Bank N-1
Bank N
.
.
.
Bank 1
Bank 2
Bank 3
Bank N-1
Bank N
.
.
.
Bank 1
Bank 2
Bank 3
Bank N-1
Bank N
.
.
.
Bank 1
Bank 2
Bank 3
Bank N-1
Bank N
New Failure
Trigger bankNew failure
Trigger failure(initialises algorithm)
Contagion rounds(algorithm internal loop)
Final Failures(algorithm converges)
New failure Newfailures…
Note: This figure depicts the dynamics of a network analysis. Starting with a matrix of interbank exposures, the analysis consists of simulating shocks to a specific institution (the trigger bank) and tracking the domino effect to other institutions in the network.
Source: IMF (2009), World Economic and Financial Surveys, Global Financial Stability Report, Responding to the Financial Crisis and Measuring Systemic Risks, International Monetary Fund, Washington, DC, available at www.imf.org/external/pubs/ft/gfsr/2009/01/pdf/chap2.pdf, p. 6.
“Policies are becoming more complex and interrelated” OECD, 2012
The context for policymaking is one where future uncertainties are unavoidable.
7
Even when much changes – replacing the old with the new - and rapidly so, some things remain the same. It comes down to ensuring the needs and interests of citizens and businesses. Achieving timeless public policy objectives …
“Sustained economic growth requires innovation, and innovation cannot be decoupled from creative destruction, which replaces the old with the new in the economic realm and also destabilizes established power relations in politics.”
Daron Acemoglu and James A. Robinson, “Why Nations Fail”
… achieving the timeless public policy objectives, such as consumer protection, good public health, financial stability, road safety, also when cars need no drivers, when money is something machines mine for, when clouds and apps run your home and life.
8
And we want to achieve those timeless public policy objectives in the most effective and efficient way: effective in resolving the problem, efficient in minimizing costs. The financial crisis was a wake up call. Better regulation became the smart regulation agenda, based on the conviction that regulation indeed has a positive and necessary role in making sure markets serve their purpose of delivering prosperity for all. Let’s ensure regulation can continue play this role, doing so in a complex, hyperconnected reality. To that end, we must complement the current focus on correcting underperforming or incomplete regulation with regulatory models for effectively and efficiently shaping developments, not pretending to control them. This is Smarter Intervention.
9
“The economic and financial problems of the last two years have contained important lessons for regulatory policy” (European Commission, 2010)
10
SMARTER INTERVENTIONIN COMPLEX SYSTEMS
Manifesto for
STOP?
OBSERVE O
RIENT DECID
E
A
CT INTERVENTION
MECHANISM
INTERVENTIONPRINCIPLE
‘SMARTER INTERVENTION’
INTERVENTIONMINDSET
Mark Fell, Managing Director, Carré & Strausswith an Introduction by
Hanne Melin, Policy Strategy Counsel, eBay Inc.
EN EN
EUROPEAN COMMISSION
Brussels, 8.10.2010 COM(2010) 543 final
COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL
COMMITTEE AND THE COMMITTEE OF THE REGIONS
Smart Regulation in the European Union
Bouis et al (2011) Jacobzone et al (2010) Kox and Nordas (2009) Djanko et al (2006) Kaufman et al (2005 Hall and Jones (1999)
11
Research shows that quality of legislation is strongly linked to economic growth and productivity.
Still, first policy responses remain fixed on traditional regulation even though it’s not necessarily the most effective or efficient approach. That’s one finding of a 2011 OECD survey of 15 EU countries. The survey finds that alternatives to regulation are not given proper attention or have they been developed significantly. Noteworthy, 2007 was the last year the European Commission discussed choices of regulatory instruments in its better lawmaking reports.
“While insisting on the potential of regulatory alternatives, the Commission’s approach also recognises that, in many cases, regulations remain the simplest way to reach EU objectives and provide business and citizens with legislative certainty.”
The choice of regulatory instruments and partnership in regulation go hand in hand. When dealing with complexity and nonlinearity, you need to tap the full potential of collaboration. Because: ! No one has the full view ! No one sits on all the information ! No one can fully control the
consequences of one’s actions
“Only on the basis of procedural primacy can the system cope with a wide variety of changing circumstances … without prior commitment to specific solutions.”
Talcott Parsons, 1966
14
For sure there is time and place for traditional top down regulation attempting change behavior by detailing how the regulated entities must act but command and control regulation must be part of a wider arsenal of regulatory models.
The 2001 White Paper on European Governance – the foundation for the better lawmaking agenda - urged the involvement of stakeholders not only in shaping but also in delivering EU policy. We have not yet been successful at that. Because we have not systematically explored the range of “intervention agents” available and, importantly, studied their strengths and weaknesses. Intervention agents may emanate from the public sector, the private sector, civil society; they may be individuals or crowds, experts or non experts, humans or algorithmic. Most likely it is a mix of these agents that we need for dancing with systems.
15
“We do not control the physiological system. We do not dictate what happens. We try to dance with the system. We add inputs to the system. We monitor. We make adjustments. But we do not have the final say over what happens. It’s a nonlinear way of dealing with the system. Not a linear one. I can look at two identical cases. Treat them exactly the same and have two different results.”
Chris Pollard, member of the Royal College of Veterinary Surgeons
16
Source: “Roadmap for the Emerging Internet of Things” by Mark Fell, Carré & Strauss
17
When law making takes place in a context of non linearity, complexity and interconnections… when no solution is final… … then we are wrong to frame the challenge as one of more or less government intervention, more or less stakeholder involvement. The challenge is - in the context of a specific issue - to identify the right mix: ! Who is best placed to gather inputs from an environment – to observe? ! Who is best equipped to make sense of the situation – to orient? ! Who should use this new knowledge as the basis for decisions? ! And, who ought to translate these decisions into action?
18
LIGHTS | CAMERA | ACTION !
Setting out a Methodology to M
eet the Challenge
MANIFESTO FOR SM
ARTER INTERVENTION IN COMPLEX SYSTEM
S
45
44
(3) DATA LIM
ITATIONS: Eric
Schmidt, Executive Chairm
an of
Google, points out that, “From
the dawn of civilization until
2003, humankind generated
!ve exabytes of data. Now we
produce !ve exabytes every
two days... and the pace is
accelerating”. 41 As a result
there is a lot excitement
surrounding the promise of
what is being termed, ‘Big
Data’ - the ability to cycle the
OODA loop in unprecedented
ways in almost every walk
of life due to advances in
technology. Nevertheless there
are signi!cant challenges to
be addressed, not least those
pertaining to privacy. Simon
Szykman, Chief Inform
ation
O"cer of the US Com
merce
Department, has outlined his
top nine Big Data challenges. 42
These are: data acquisition;
storage; processing; data
transport and dissemination;
data managem
ent and curation;
archiving; security; workforce
with specialized skills, and; the
cost of all of the above.
(4) ECONOM
IC LIMITATIONS:
Sometim
es it simply m
akes
no !nancial sense to try to
develop complex software
algorithms to solve a problem
that a human can solve instantly
and e#ortlessly. Lanier notes
that until recently, computers
couldn’t even recognize a
person’s smile. 43
As a result, leading arti!cial intelligence
researchers, such as Professor Hans Moravec
from the Robotics Institute at Carnegie
Mellon University, acknowledge that
“Computers have far to go to m
atch human
strengths”. 44
Lanier recalls that “Before the crash, bankers
believed in supposedly intelligent algorithms
that could calculate credit risks before making
bad loans.” 45
Indeed Lanier is careful to draw the
distinction between ideal and real
computers. 46
Nevertheless this is not to say that “comm
on
sense” approaches do not have their own
shortcomings.
Watts identi!es three lim
itations in comm
on
sense reasoning: 47
(1) INDIVIDUAL BEHAVIOUR: Our
mental m
odels systematically
fall short of capturing the
complexity of what drives
individual behaviour.
(2) COLLECTIVE BEHAVIOUR: Our
mental m
odels of collective
behaviour fail to take account of
the fact that collective behaviour
is greater than the sum of its
parts.
(3) LEARNING FROM
HISTORY:
We learn less from
the past
than we think we do and this
misperception in turn skews our
perception of the future.
Non-Expert
Expert
Individual
Crowd
Algorithms
Public
Sector
Private
Sector
AGENT
CONFIGURATION
AGENT ORIGIN
AGENT
TYPE
EACH OF THESE
CUBES IS A POTENTIAL
“INTERVENTION AGENT”
FIGURE 15 - THE FULL ‘INTERVENTION MIX’ IN 3D
‘As well as there being di!erent intervention types and con"gurations, intervention can also
originate from the private and public sectors’
Source: Carré & Strauss
Watts concludes that: 48
“Comm
onsense reasoning, therefore,
does not su"er from a single
overriding limitation but rather from
a combination of lim
itations, all of
which reinforce and even disguise one
another. The net result is that comm
on
sense is wonderful at making sense
of the world, but not necessarily at
understanding it.”
PUBLIC VERSUS PRIVATE SECTOR
INTERVENTION
We have covered the strengths and
weaknesses of the various ‘Intervention
Agents’. We now need to introduce the third
dimension to the ‘Intervention M
ix’, namely
the division between public and private
sector intervention.
For both sectors can deploy all of these
agents. Think of general elections and
referendums (‘wisdom
of crowds’),
LIGHTS | CAMERA | ACTION !
Setting out a Methodology to Meet the Challenge
MANIFESTO FOR SMARTER INTERVENTION IN COMPLEX SYSTEMS
45
44
(3) DATA LIMITATIONS: Eric
Schmidt, Executive Chairman of
Google, points out that, “From
the dawn of civilization until
2003, humankind generated
!ve exabytes of data. Now we
produce !ve exabytes every
two days... and the pace is
accelerating”. 41 As a result
there is a lot excitement
surrounding the promise of
what is being termed, ‘Big
Data’ - the ability to cycle the
OODA loop in unprecedented
ways in almost every walk
of life due to advances in
technology. Nevertheless there
are signi!cant challenges to
be addressed, not least those
pertaining to privacy. Simon
Szykman, Chief Information
O"cer of the US Commerce
Department, has outlined his
top nine Big Data challenges. 42
These are: data acquisition;
storage; processing; data
transport and dissemination;
data management and curation;
archiving; security; workforce
with specialized skills, and; the
cost of all of the above.
(4) ECONOMIC LIMITATIONS:
Sometimes it simply makes
no !nancial sense to try to
develop complex software
algorithms to solve a problem
that a human can solve instantly
and e#ortlessly. Lanier notes
that until recently, computers
couldn’t even recognize a
person’s smile. 43
As a result, leading arti!cial intelligence
researchers, such as Professor Hans Moravec
from the Robotics Institute at Carnegie
Mellon University, acknowledge that
“Computers have far to go to match human
strengths”. 44
Lanier recalls that “Before the crash, bankers
believed in supposedly intelligent algorithms
that could calculate credit risks before making
bad loans.” 45
Indeed Lanier is careful to draw the
distinction between ideal and real
computers. 46
Nevertheless this is not to say that “common
sense” approaches do not have their own
shortcomings.
Watts identi!es three limitations in common
sense reasoning: 47
(1) INDIVIDUAL BEHAVIOUR: Our
mental models systematically
fall short of capturing the
complexity of what drives
individual behaviour.
(2) COLLECTIVE BEHAVIOUR: Our
mental models of collective
behaviour fail to take account of
the fact that collective behaviour
is greater than the sum of its
parts.
(3) LEARNING FROM HISTORY:
We learn less from the past
than we think we do and this
misperception in turn skews our
perception of the future.
Non-Expert
Expert
Individual
Crowd
Algorithms
Public
Sector
Private
Sector
AGENT
CONFIGURATION
AGENT ORIGIN
AGENT
TYPE
EACH OF THESE
CUBES IS A POTENTIAL
“INTERVENTION AGENT”
FIGURE 15 - THE FULL ‘INTERVENTION MIX’ IN 3D
‘As well as there being di!erent intervention types and con"gurations, intervention can also
originate from the private and public sectors’
Source: Carré & Strauss
Watts concludes that: 48
“Commonsense reasoning, therefore,
does not su"er from a single
overriding limitation but rather from
a combination of limitations, all of
which reinforce and even disguise one
another. The net result is that common
sense is wonderful at making sense
of the world, but not necessarily at
understanding it.”
PUBLIC VERSUS PRIVATE SECTOR
INTERVENTION
We have covered the strengths and
weaknesses of the various ‘Intervention
Agents’. We now need to introduce the third
dimension to the ‘Intervention Mix’, namely
the division between public and private
sector intervention.
For both sectors can deploy all of these
agents. Think of general elections and
referendums (‘wisdom of crowds’),
LIGH
TS | CAMERA | ACTIO
N !
Setting out a Methodology to M
eet the Challenge
MAN
IFESTO FO
R SMARTER IN
TERVENTIO
N IN
COM
PLEX SYSTEMS
45
44
(3) D
ATA LIM
ITATION
S: Eric Schm
idt, Executive Chairman of
Google, points out that, “From
the daw
n of civilization until 2003, hum
ankind generated !ve exabytes of data. Now
we
produce !ve exabytes every tw
o days... and the pace is accelerating”. 41 As a result there is a lot excitem
ent surrounding the prom
ise of w
hat is being termed, ‘Big
Data’ - the ability to cycle the
OO
DA loop in unprecedented
ways in alm
ost every walk
of life due to advances in technology. N
evertheless there are signi!cant challenges to be addressed, not least those pertaining to privacy. Sim
on Szykm
an, Chief Information
O"
cer of the US Com
merce
Departm
ent, has outlined his top nine Big D
ata challenges. 42 These are: data acquisition; storage; processing; data transport and dissem
ination; data m
anagement and curation;
archiving; security; workforce
with specialized skills, and; the
cost of all of the above.(4)
ECON
OM
IC LIMITATIO
NS:
Sometim
es it simply m
akes no !nancial sense to try to develop com
plex software
algorithms to solve a problem
that a hum
an can solve instantly and e#ortlessly. Lanier notes that until recently, com
puters couldn’t even recognize a person’s sm
ile. 43
As a result, leading arti!cial intelligence researchers, such as Professor H
ans Moravec
from the Robotics Institute at Carnegie
Mellon U
niversity, acknowledge that
“Computers have far to go to m
atch human
strengths”. 44
Lanier recalls that “Before the crash, bankers believed in supposedly intelligent algorithm
s that could calculate credit risks before m
aking bad loans.”
45
Indeed Lanier is careful to draw the
distinction between ideal and real
computers. 46
Nevertheless this is not to say that “com
mon
sense” approaches do not have their own
shortcomings.
Watts identi!es three lim
itations in comm
on sense reasoning: 47
(1) IN
DIVID
UAL BEH
AVIOU
R: Our
mental m
odels systematically
fall short of capturing the com
plexity of what drives
individual behaviour. (2)
COLLECTIVE BEH
AVIOU
R: Our
mental m
odels of collective behaviour fail to take account of the fact that collective behaviour is greater than the sum
of its parts.
(3) LEA
RNIN
G FRO
M H
ISTORY:
We learn less from
the past than w
e think we do and this
misperception in turn skew
s our perception of the future.
Non-Expert
ExpertIndividual
Crowd
Algorithms
PublicSector
PrivateSector
AGEN
TCO
NFIG
URATIO
N
AGENT ORIGIN
AGEN
T TYPE
EACH O
F THESE
CUBES IS A
POTEN
TIAL
“INTERVEN
TION
AGEN
T”
FIGU
RE 15 - THE FU
LL ‘INTERVEN
TION
MIX’ IN
3D‘As w
ell as there being di!erent intervention types and con"gurations, intervention can also
originate from the private and public sectors’
Source: Carré & Strauss
Watts concludes that: 48
“Comm
onsense reasoning, therefore, does not su"er from
a single overriding lim
itation but rather from
a combination of lim
itations, all of w
hich reinforce and even disguise one another. The net result is that com
mon
sense is wonderful at m
aking sense of the w
orld, but not necessarily at understanding it.”
PUBLIC VERSU
S PRIVATE SECTOR
INTERVEN
TION
We have covered the strengths and
weaknesses of the various ‘Intervention
Agents’. We now
need to introduce the third dim
ension to the ‘Intervention Mix’, nam
ely the division betw
een public and private sector intervention.
For both sectors can deploy all of these agents. Think of general elections and referendum
s (‘wisdom
of crowds’),
LIGHTS | CAMERA | ACTION !
Setting out a Methodology to M
eet the Challenge
MANIFESTO FOR SM
ARTER INTERVENTION IN COMPLEX SYSTEM
S
45
44
(3) DATA LIM
ITATIONS: Eric
Schmidt, Executive Chairm
an of
Google, points out that, “From
the dawn of civilization until
2003, humankind generated
!ve exabytes of data. Now we
produce !ve exabytes every
two days... and the pace is
accelerating”. 41 As a result
there is a lot excitement
surrounding the promise of
what is being term
ed, ‘Big
Data’ - the ability to cycle the
OODA loop in unprecedented
ways in almost every walk
of life due to advances in
technology. Nevertheless there
are signi!cant challenges to
be addressed, not least those
pertaining to privacy. Simon
Szykman, Chief Inform
ation
O"cer of the US Com
merce
Department, has outlined his
top nine Big Data challenges. 42
These are: data acquisition;
storage; processing; data
transport and dissemination;
data managem
ent and curation;
archiving; security; workforce
with specialized skills, and; the
cost of all of the above.
(4) ECONOM
IC LIMITATIONS:
Sometim
es it simply m
akes
no !nancial sense to try to
develop complex software
algorithms to solve a problem
that a human can solve instantly
and e#ortlessly. Lanier notes
that until recently, computers
couldn’t even recognize a
person’s smile. 43
As a result, leading arti!cial intelligence
researchers, such as Professor Hans Moravec
from the Robotics Institute at Carnegie
Mellon University, acknow
ledge that
“Computers have far to go to m
atch human
strengths”. 44Lanier recalls that “Before the crash, bankers
believed in supposedly intelligent algorithms
that could calculate credit risks before making
bad loans.”45
Indeed Lanier is careful to draw the
distinction between ideal and real
computers. 46
Nevertheless this is not to say that “comm
on
sense” approaches do not have their own
shortcomings.
Watts identi!es three lim
itations in comm
on
sense reasoning: 47(1)
INDIVIDUAL BEHAVIOUR: Our
mental m
odels systematically
fall short of capturing the
complexity of w
hat drives
individual behaviour.
(2) COLLECTIVE BEHAVIOUR: Our
mental m
odels of collective
behaviour fail to take account of
the fact that collective behaviour
is greater than the sum of its
parts. (3)
LEARNING FROM HISTORY:
We learn less from
the past
than we think we do and this
misperception in turn skews our
perception of the future.
Non-ExpertExpert
Individual
Crowd
Algorithms
PublicSector Private
Sector
AGENTCONFIGURATION
AGENT ORIGIN
AGENT TYPE
EACH OF THESE
CUBES IS A POTENTIAL
“INTERVENTION AGENT” FIGURE 15 - THE FULL ‘INTERVENTION MIX’ IN 3D
‘As well as there being di!erent intervention types and con"gurations, intervention can also
originate from the private and public sectors’
Source: Carré & Strauss
Watts concludes that: 48“Com
monsense reasoning, therefore,
does not su"er from a single
overriding limitation but rather from
a combination of lim
itations, all of
which reinforce and even disguise one
another. The net result is that comm
on
sense is wonderful at making sense
of the world, but not necessarily at
understanding it.”
PUBLIC VERSUS PRIVATE SECTOR
INTERVENTIONW
e have covered the strengths and
weaknesses of the various ‘Intervention
Agents’. We now
need to introduce the third
dimension to the ‘Intervention M
ix’, namely
the division between public and private
sector intervention.
For both sectors can deploy all of these
agents. Think of general elections and
referendums (‘w
isdom of crowds’),
LIGHTS | C
AMERA | ACTIO
N !
Setting out a
Meth
odology to M
eet the Challe
nge
MANIFESTO FOR SMARTER IN
TERVENTION IN
COMPLEX SYSTEMS
45
44
(3) DATA LIM
ITATIONS: E
ric
Schmidt, E
xecu
tive Chairm
an of
Google, points
out that,
“From
the dawn of c
ivilizatio
n until
2003, humankind generated
!ve exabytes of d
ata. Now w
e
produce !ve exabytes every
two days...
and the pace is
accelerating”.41
As a
resu
lt
there is
a lot e
xcite
ment
surro
unding the pro
mise of
what is b
eing term
ed, ‘Big
Data’ -
the abilit
y to cy
cle th
e
OODA loop in
unprecedented
ways in
almost
every
walk
of life due to
advance
s in
technology.
Neve
rtheless
there
are signi!ca
nt challe
nges to
be addressed, n
ot least
those
pertaining to
privacy
. Sim
on
Szykman, C
hief Inform
ation
O"cer o
f the U
S Commerce
Department, h
as outlin
ed his
top nine Big D
ata ch
allenges.42
These are: d
ata acq
uisitio
n;
storage; p
roce
ssing; d
ata
transp
ort and diss
emination;
data m
anagement and cu
ration;
archiving; se
curit
y; workf
orce
with sp
ecializ
ed skills
, and; th
e
cost
of all o
f the above
.
(4) ECONOMIC
LIMITATIO
NS:
Sometimes it
simply
makes
no !nancial se
nse to
try t
o
develop co
mplex softw
are
algorithms t
o solve
a problem
that
a human can so
lve in
stantly
and e#ortlessl
y. La
nier notes
that
until rece
ntly, c
omputers
couldn’t e
ven re
cognize
a
person’s s
mile.43
As a re
sult,
leading arti!cia
l intellig
ence
research
ers, su
ch as P
rofesso
r Hans M
oravec
from th
e Robotics I
nstitu
te at Carn
egie
Mellon U
niversi
ty, ack
nowledge that
“Computers
have far to
go to m
atch human
strength
s”.44
Lanier r
ecalls
that
“Before th
e crash, b
ankers
believed in
supposedly in
telligent a
lgorithms
that c
ould calculate credit r
isks b
efore making
bad loans.”45
Indeed Lanier is
careful to
draw th
e
distincti
on between id
eal and re
al
computers.46
Neverth
eless th
is is n
ot to sa
y that
“common
sense
” appro
aches d
o not hav
e their o
wn
shortc
omings.
Watts id
enti!es t
hree limita
tions in
common
sense
reaso
ning:47
(1) IN
DIVID
UAL BEHAVIOUR: O
ur
mental models s
ystemati
cally
fall short
of captu
ring th
e
complexit
y of w
hat driv
es
individual behav
iour.
(2) COLLECTIV
E BEHAVIOUR: O
ur
mental models o
f colle
ctive
behaviour fa
il to ta
ke acc
ount of
the fa
ct th
at co
llecti
ve behav
iour
is greate
r than th
e sum of it
s
parts.
(3) LEARNIN
G FROM HISTORY:
We learn
less
from th
e past
than w
e think w
e do and this
misperce
ption in
turn
skews o
ur
perceptio
n of the fu
ture.
Non-Expert
Expert
Individ
ual
Crowd
Algorithms
Public Sector
Private
Sector
AGENT
CONFIGURATIO
N
AGENT ORIGIN
AGENT
TYPE
EACH OF THESE
CUBES IS A POTENTIA
L
“INTERVENTIO
N AGENT”
FIGURE 15 - T
HE FULL ‘INTERVENTIO
N MIX
’ IN 3D
‘As well a
s there being di!erent in
tervention ty
pes and con"gura
tions, i
ntervention can also
originate fro
m the priv
ate and public se
ctors’
Source: C
arré & Stra
uss
Watts c
oncludes t
hat:48
“Commonsense re
asoning, th
erefore,
does not s
u"er from a sin
gle
overriding lim
itatio
n but rath
er from
a combination of li
mitatio
ns, all o
f
which reinforce and even disg
uise one
another.
The net resu
lt is t
hat common
sense is wonderfu
l at m
aking sense
of the w
orld, b
ut not n
ecessarily
at
understanding it.”
PUBLIC VERSUS PRIVATE SECTOR
INTERVENTIO
N
We have co
vered th
e strength
s and
weaknesses o
f the va
rious ‘I
nterventio
n
Agents’. W
e now need to in
troduce
the th
ird
dimensio
n to th
e ‘Interve
ntion M
ix’, namely
the divisi
on between public
and private
secto
r interve
ntion.
For b
oth se
ctors
can deploy a
ll of th
ese
agents. T
hink of g
eneral electi
ons and
referendums (‘w
isdom of c
rowds’)
,
LIGHTS | CAMERA | A
CTION !
Setting out a
Methodology to
Meet th
e Challenge
MANIFESTO FOR SMARTER INTERVENTIO
N IN COMPLEX SYSTEMS
45
44
(3) DATA LIM
ITATIONS: E
ric
Schmidt, Executiv
e Chairman of
Google, points
out that, “
From
the dawn of civilizatio
n until
2003, humankind generated
!ve exabytes of d
ata. Now we
produce !ve exabytes every
two days...
and the pace is
accelerating”.41
As a
resu
lt
there is a lo
t excite
ment
surro
unding the promise
of
what is being te
rmed, ‘B
ig
Data’ - the abilit
y to cycle th
e
OODA loop in
unprecedented
ways in alm
ost every w
alk
of life due to
advances in
technology. Neverth
eless there
are signi!cant c
hallenges t
o
be addressed, n
ot least
those
pertaining to
privacy.
Simon
Szykman, Chief In
formatio
n
O"cer of th
e US Commerce
Department, h
as outlin
ed his
top nine Big Data challenges.42
These are: data acquisit
ion;
storage; p
rocessing; d
ata
transp
ort and diss
emination;
data management a
nd curation;
archiving; security
; workforce
with sp
ecialized sk
ills, and; th
e
cost of a
ll of th
e above.
(4) ECONOMIC LIM
ITATIONS:
Sometimes it
simply m
akes
no !nancial sense to
try to
develop complex softw
are
algorithms t
o solve a problem
that a human can so
lve insta
ntly
and e#ortlessl
y. Lanier n
otes
that until
recently, computers
couldn’t even re
cognize a
person’s s
mile.43
As a re
sult,
leading arti!cial in
telligence
researchers, su
ch as Professo
r Hans M
oravec
from th
e Robotics In
stitute at C
arnegie
Mellon Universi
ty, acknowledge th
at
“Computers have fa
r to go to
match human
strengths”.44
Lanier recalls
that “Before th
e crash, bankers
believed in su
pposedly intellig
ent algorith
ms
that could calculate credit risk
s before m
aking
bad loans.”45
Indeed Lanier is careful to
draw the
distinctio
n between id
eal and re
al
computers.46
Nevertheless
this is n
ot to sa
y that “c
ommon
sense” approaches d
o not have th
eir own
shortc
omings.
Watts id
enti!es t
hree limita
tions in
common
sense reaso
ning:47
(1) IN
DIVIDUAL BEHAVIO
UR: Our
mental models s
ystematic
ally
fall short o
f capturin
g the
complexity of w
hat driv
es
individual behaviour.
(2) COLLECTIVE BEHAVIO
UR: Our
mental models o
f colle
ctive
behaviour fail t
o take account o
f
the fact th
at colle
ctive behaviour
is greater th
an the su
m of its
parts.
(3) LEARNIN
G FROM HISTORY:
We learn le
ss fro
m the past
than we th
ink we do and th
is
misperceptio
n in tu
rn skews o
ur
perception of th
e future.
Non-Expert
Expert
Individual
Crowd
Algorithms
Public Sector
Private Sector
AGENT
CONFIGURATIO
N
AGENT ORIGIN
AGENT TYPE
EACH OF THESE
CUBES IS A POTENTIAL
“INTERVENTIO
N AGENT”
FIGURE 15 - T
HE FULL ‘INTERVENTIO
N MIX’ IN
3D
‘As well as th
ere being di!erent interventio
n types and con"guratio
ns, interventio
n can also
originate fro
m the priv
ate and public sectors’
Source: Carré
& Strauss
Watts concludes t
hat:48
“Commonsense reasoning, th
erefore,
does not su
"er from a sin
gle
overriding lim
itatio
n but rather fr
om
a combination of li
mitatio
ns, all o
f
which reinforce and even disg
uise one
another. The net re
sult is th
at common
sense is wonderfu
l at m
aking sense
of the world
, but not n
ecessarily
at
understanding it.”PUBLIC VERSUS PRIVATE SECTOR
INTERVENTIO
N
We have covered the st
rengths and
weaknesses o
f the vario
us ‘Interventio
n
Agents’. W
e now need to in
troduce th
e third
dimensio
n to th
e ‘Interventio
n Mix’, n
amely
the division betw
een public and priv
ate
sector interventio
n.
For both se
ctors can deploy all o
f these
agents. Think of g
eneral electio
ns and
referendums (‘wisd
om of crowds’),
LIGHTS | CAMERA | ACTION !
Setting out a Methodology to Meet the Challenge
MANIFESTO FOR SMARTER INTERVENTION IN COMPLEX SYSTEMS
45
44
(3) DATA LIMITATIONS: Eric
Schmidt, Executive Chairman of
Google, points out that, “From
the dawn of civilization until
2003, humankind generated
!ve exabytes of data. Now we
produce !ve exabytes every
two days... and the pace is
accelerating”.41 As a result
there is a lot excitement
surrounding the promise of
what is being termed, ‘Big
Data’ - the ability
to cycle the
OODA loop in unprecedented
ways in almost every walk
of life due to advances in
technology. Nevertheless there
are signi!cant challenges to
be addressed, not least those
pertaining to privacy. Simon
Szykman, Chief Information
O"cer of the US Commerce
Department, has outlined his
top nine Big Data challenges.42
These are: data acquisition;
storage; processing; data
transport and dissemination;
data management and curation;
archiving; security; workforce
with specialized skills, and; the
cost of all of the above.
(4) ECONOMIC LIMITATIONS:
Sometimes it simply makes
no !nancial sense to try to
develop complex software
algorithms to solve a problem
that a human can solve instantly
and e#ortlessly. Lanier notes
that until recently, computers
couldn’t even recognize a
person’s smile.43
As a result, leading arti!cial intelligence
researchers, such as Professor Hans Moravec
from the Robotics Institute at Carnegie
Mellon University, acknowledge that
“Computers have far to go to match human
strengths”.44 Lanier recalls that “Before the crash, bankers
believed in supposedly intelligent algorithms
that could calculate credit risks before making
bad loans.”45 Indeed Lanier is careful to draw the
distinction between ideal and real
computers.46 Nevertheless this is not to say that “common
sense” approaches do not have their own
shortcomings. Watts identi!es three limitations in common
sense reasoning:47 (1) INDIVIDUAL BEHAVIOUR: Our
mental models systematically
fall short of capturing the
complexity of what drives
individual behaviour.
(2) COLLECTIVE BEHAVIOUR: Our
mental models of collective
behaviour fail to take account of
the fact that collective behaviour
is greater than the sum of its
parts. (3) LEARNING FROM HISTORY:
We learn less from the past
than we think we do and this
misperception in turn skews our
perception of the future.
Non-ExpertExpert
Individual
Crowd
Algorithms
Public Sector
Private SectorAGENT
CONFIGURATION
AGENT ORIGIN
AGENT TYPE
EACH OF THESE
CUBES IS A POTENTIAL
“INTERVENTION AGENT”
FIGURE 15 - THE FULL ‘INTERVENTION MIX’ IN 3D
‘As well as there being di!erent intervention types and con"gurations, intervention can also
The OECD finds that countries are failing to close the loop and make connections between design, implementation and evaluation. The Boyd Loop– a decision making tool first developed for fighter pilots – could be a tool for full-cycle policymaking under uncertainty. We should frame the challenge as one of using intervention agents – mixing types, configurations and origin – to cycle the Boyd Loop most effectively. But we appear to be stuck at the false choice of more or less regulation. The OECD finds policymakers reluctant to seriously consider the use of alternatives to traditional top down regulation. To overcome reluctance, perhaps we need to revisit the role of the policymaker…
19 20
Disruptor
Encourager
Sense-Maker
Source: Plowman et al (2007), “The role of leadership in emergent, self-organization”
Let’s suggest that policymakers shouldn’t think of themselves as a “commander and controller”, but as a leader of emergent, self-organizing, complex organizations. Policymakers would then seek to affect change by: ! Disrupting existing patterns of behavior to
enable new ideas to emerge ! Making sense of change and unfolding
events, interpreting rather than creating ! Encouraging other stakeholders to be
innovative in the public policy space rather than assuming that responsibility
Three roles for the policymaker where action is about enabling instead of controlling behavior.
“to cope with interconnectedness of sectors and economies”
“a preference for performance-based regulation” “meeting policy objectives in complex or rapidly changing policy environments”
OECD, 2012 Recommendations
21
The enabling regulator would be well equipped to act on the 2012 OECD recommendation on regulatory policy, which urges a preference for performance based regulation. Performance-based regulation specifies objectives or "output standards", but does not specify how to achieve compliance. It sets a general goal and lets each regulated entity decide how to meet it. And so the regulated entities may find ways of achieving the goal which had not been thought of by the regulator, when ! attention is shifted from the letter of the law to results ! over or under inclusive rules become more future proof principles ! neither regulator nor regulated entities can rely on check lists ! both regulator and regulated entities can more easily adapt to technological change and
new hazards More than ten years ago a Harvard university workshop with experts from agencies of diverse sectors concluded that performance-based regulation holds promise for achieving health, safety, and environmental goals at lower cost, and in a way that accommodates - even encourages - technological innovation. Still, performance based regulation is used much less than one would expect….
22
It is used much less than one should expect because with flexibility comes discretion, comes fear of legal uncertainty, of unpredictability, of loosing control. And so it comes down to being able to acknowledge and absorb uncertainty. Enter the Boyd Loop! And couple it with the capacity to specify, measure, and monitor performance. This is how most modern businesses operate today: solving problems and making decisions based on performance metrics and data analytics, constant monitoring and readjustments. It is survivable trial and error. And it is something we can achieve in policymaking by matching performance based regulation with modern data analytics.
23 24
PAYPAL 21ST CENTURY REGULATION: PUTTING INNOVATION AT THE HEART OF PAYMENTS REGULATION
STEP 3 Algorithms are created and applied to glean insights from the database – Decide.
Algorithm is a term that is widely misunderstood and is treated as far too technical for the average policymaker. But, this could not be further from the truth. An algorithm is merely a set of rules to be followed during an operation. An algorithm is basically an instruction manual. In the case of big data, an algorithm enables us to answer questions, or glean insights, from the database.
An example should help to further clarify. If there is a government health database containing information on blood types. If we wanted to know what the most common blood type is in the country, an algorithm that merely adds up all of the different blood types and then ranks them could be created to respond to the question.
It is important to recognize what big data can tell us and what it can’t. Big data lets us discover correlations (what is happening) rather than causation (why something is happening). Correlations allow us to capture the present and predict, with a certain likelihood, the future. Experts will be essential in designing algorithms and helping to interpret the insights from the results. Moreover, at this stage of the cycle, policy innovation includes using the data insights for thinking more broadly about where and how to introduce change into a system in order to achieve the set regulatory goals.
STEP 4 Review and Readjust the data gathering, the database organization, and the algorithms (Constantly) – Feedback.
The key to success for entities using modern data analysis techniques is to be able to constantly innovate and adjust to the rapidly changing environment by generating and receiving feedback from current and previous iterations. (Figure 14. Dynamic Repetition and Adjustment). There is no reason why government regulators cannot be equally agile in terms of both their processes and means of achieving their objectives. In fact, they should be. This new regulatory model requires timely, plentiful and compelling feedback loops.
Feedback loops will also adjust the mechanics of the model: If gathering a particular piece of data does not help to achieve the goals that the regulator is seeking to achieve then it should no longer be requested. If databases are not structured in an efficient manner or are not integrated then the system must be reformed. Finally, if an algorithm is not leading to meaningful insights then the calculations must be readjusted. Moreover, proper feedback loops are essential in order to avoid the trap of misuse as well as overreliance on data.
FIGURE 13 A PAYMENT SYSTEMS MARKET EXPERT GROUP
FIGURE 14 DYNAMIC PEPETITION AND ADJUSTMENT
• A bridge to tomorrow’s Complex Systems Big Data Mindset
• Made up of regulatory, issue-specific and technical experts
• Tasked with implementing SMART governance including the feedback loops to ensure constant readjustment
• Aligned, constituted and governed according to its terms of reference
• Tasked with monitoring (prevent too much fishing expeditions, warn against overreliance/misuse) and ensuring transparency of – and proper information flows within - SMART Governance Cycle
Secure large amounts of data
Marchines organize data
Algorithms glean insights
Readjuststeps 1-3
(constantly)
Target insightstowards goals
1
2
3
5
4
20
Source: PayPal, 21st Century Regulation, 2013
Continuously and in real time, we can: ! Collect relevant performance data
from the regulated entities ! Organize and unify it with other data
sources ! Use algorithms to analyze and learn
from it ! And take those insights to inform the
course of action
25
Building ground for partnership in regulation by leveraging innovation: 1. The very decision to consider performance standards can shake things up.
This is the disruptor: surfacing underlying uncertainties, exposing actual performance, and focusing attention on goals.
2. A performance based approach rests on dialogue, on regulatory conversation. You can increase certainty through an interpretive community, you can enhance compliance by changing perception. This is the sense maker.
3. Data points, metrics and algorithms can create a common language and a common – depoliticized - understanding of output and expectations. And we can set expectations because we know the strengths and weaknesses of the various intervention agents.
With a common understanding of goals, regulators are also better positioned to exercise their role of encourager: encouraging continuous improvements by combining performance standards with incentives.
“a depoliticised regulatory process might produce better results”
Justice Stephen Breyer, Supreme Court of the United States (1993)
