Policy Compass WorkshopProject Introduction

•1st June 2015 URBUN

Agenda

Today! - Policy Compass Project Workshop

Introduction of Policy Compass Project

Methodology - Three Pillars of Policy Compass

Policy Impact Modeling using Fuzzy Cognitive Map (FCM)

in Policy Compass – Two Case Study

Platform Demo

Q&A with short survey

2

Introduction of Policy Compass Project

• Policy Compass Project (FP7-ICT)(September 2013~ September 2016, 36 Month)

26 March 2015 WP4 – UBRUN, UK 3

www.policycompasss.eu

Introduction of Policy Compass Project

4WP4 – Berlin, Germany10 February 2015

• The main goal of Policy Compass is to develop a research prototype of an easy-to-use, highly visual and intuitive tool for social networks and eParticipation platforms, enabling citizens and public officials to easily create, apply, share, embed, annotate and discuss causal models, charts and graphs of historical data from trusted open data sources. The aim is to develop methods and tools that facilitate more factual, evidence-based, transparent and accountable policy evaluation and analysis.

• How to realize this goal?Let’s visit the platform after presentation(alpha version)

alpha.policycompass.eu

Official Page

policycompass.eu

Introduction of Policy Compass Project

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Three Pillars of Policy Compass

• The Policy Compass methodology pillars represent different views on the Policy Compass platform centred on the particular interests of typical end users

• Evaluating Performance of Policies (EPP)

• Building Causal Policy Models (BCPM)

• Policy Impact Deliberation and Argument Mapping (PIDAM)

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1st Pillar - Evaluating Performance of Policies (EPP)

• EPP.1 Data discovery and processing• EPP.2 Metrics definition.

UBRUN, UK 7

1st Pillar - Evaluating Performance of Policies (EPP)

• EPP.3 Metrics operationalization, visualisation and comparison

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1st Pillar - Evaluating Performance of Policies (EPP)

• EPP.4 Improving understanding of the data.

• EPP.5 Seeking explanation of policy impacts.

=>Next Pillar

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2nd Pillar - Building Causal Policy Models (BCPM)

• BCPM 1. Creating/refining causal models.

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2nd Pillar - Building Causal Policy Models (BCPM)

• BCPM 2. Turning causal networks to Fuzzy Cognitive Maps.• What is FCMs?

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A weight wij describes the strength of causality between two concepts.

2nd Pillar - Building Causal Policy Models (BCPM)

• BCPM 3. Simulating FCMs and visualising impacts.

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2nd Pillar - Building Causal Policy Models (BCPM)

• BCPM 4. Sharing knowledge and results.• Both the FCM models and the simulation results can be saved under a

user account and shared with others via social networks and web 2.0 channels.

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0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20

0.20 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46

0.80 0.55 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61

0.20 0.67 0.62 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63

3rd Pillar - Policy Impact Deliberation and Argument Mapping

• PIDAM.1 Initiate and participate in deliberations using Adhocracy• Adhocracy was conceived as a cooperative discourse, delegation and

voting software, which enables a collective, binding decision-making process online.

• PIDAM.2 Transform structured discussions into argument maps using Carneades

• PIDAM.3 Navigate argument maps using Carneades

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Introduction of Policy Compass Project

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1st Pillar

2nd Pillar

3rd Pillar

Policy Impact Modeling using Fuzzy Cognitive Map (FCM) technique in Policy Compass

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• Formal Representation

A weight wij describes the strength of causality between two concepts.

Fuzzy Cognitive Map (FCM) technique and Policy Compass

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• Obtaining Fuzzy Value • by Experts’ judgment• Or based on historical data

Very high -> 1

High -> 0.8

Medium -> 0.6

Low -> 0.4

Very Low -> 0.2

1 1 1 1 1 0.6 0.4 0.2 0.2 0.2 Fuzzified value

Fuzzy Cognitive Map (FCM) technique and Policy Compass

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• Weight between concepts• by Experts’ judgment• Or based on historical data – Automatic option

C1

C3

C2 C1 C2 C3

C1 0 1 1

C2 0 0 1

C3 0 0 0<Relationship matrix>

t0 t1 t2 t3

C1 0.2 0.4 0.2 0.2

C2 0.8 0.6 0.6 0.4

C3 0.2 0.4 0.6 0.8

<Status vector based on historical data>

C1 C2 C3

C1 0 w12 w13

C2 0 0 w23

C3 0 0 0<Weight matrix>

w12

w13w23

Policy Impact Evaluation with FCM – usage example (1)

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• Let assume that policy maker in government now want to know the future impact of change in interest rate to stimulate the productive investment.

Present State of Concepts

Interest rate: 0.4 (low)

Productive Investments: 0.2 (very low)

Occupation: 0.8 (high)

Inflation: 0.2 (very low)

Policy Impact Evaluation with FCM – usage example (1)

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• Possible scenarios for a policy maker • Situation 1. If the interest rate is kept in the same level in the future• Situation 2. If the interest rate decreases to date• Situation 3. Or if the interest rate increases

Present State of Concepts

Interest rate: 0.4 (low)

Productive Investments: 0.2 (very low)

Occupation: 0.8 (high)

Inflation: 0.2 (very low)

Policy Impact Evaluation with FCM – usage example (1)

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<Result for Situation 1>

<Result for Situation 2>

Policy Impact Evaluation with FCM – usage example (1)

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<Result for Situation 3>

<Comparing the Simulation Results>

Decision Future Value of Productive Investment Future Value of InflationKeep the interest rate as it is 0.4206757 0.6106903Decreasing the interest rate 0.4600851 0.6117947Increasing the interest rate 0.3452465 0.6085645

Policy Impact Evaluation with FCM – usage example (2)

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• Drug Policy – Strengthen or Weaken the probation against drug?• What will happen in the future ?

Policy Impact Evaluation with FCM – usage example (2)

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• Drug Policy – Strengthen or Weaken the probation against drug?

Very High “National prohibition” Very Low “National prohibition”

Policy Impact Evaluation with FCM – usage example (2)

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National prohibition

Drug acreage

Gaining Syndicates Availability of drugs

Streetgangs Use of drug Drug price

0.8 (Higher) 0.5729 0.5872 0.5878 0.7054 0.680272 0.5116 0.71000.2 (Lower) 0.5714 0.5891 0.5753 0.7210 0.599626 0.5561 0.6078

• Comparison between two cases

vs.

DEMOQ&A with Short survey

26•1st June 2015