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Value-driven policy-making as a socio-cognitive technical system - - PowerPoint PPT Presentation

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Value-driven policy-making as a socio-cognitive technical system Perell-Moragues, Antonio Noriega, Pablo Padget, Julian Verhagen, Harko First international workshop on socio-cognitive technical systems 17/07/2018 1. Motivation

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Value-driven policy-making as a socio-cognitive technical system

Perelló-Moragues, Antonio Noriega, Pablo Padget, Julian Verhagen, Harko First international workshop on socio-cognitive technical systems 17/07/2018

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  • 1. Motivation

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“Britain’s water policits are relatively benign. Not so in many other parts of a densely populated world, where the availability of clean, potable water, and water for agricultural and industrial use is a hot political, security and economic issue – as well as a frequently unmet, basic human need [...] for some, it is a cause for war”.

The Observer July 8 2018 [1]

Grand Ethiopian Renaissance Dam ▶ Ethiopia: prestige project, symbolising and facilitating the country’s development. ▶ Sudan: stability, cheap energy and reliable water supply ▶ Egypt: major threat

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  • 1. Motivation

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▶ Public policy and ethics ○ Conciliate legitimate conflicting stakeholders’ values. ○ Agree upon a better future state of the world and the means to achieve it. ○ Consequently, stakeholders commit to contribute towards the values embedded in the policy. ▶ Ethics and AI ○ Policy-design as an example of value-driven action. ■ Acting according to values ■ Foster values in a social system ○ Value-driven simulation as a tool for value-based agreements. ○ A contribution towards value-alignment AI challenge.

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  • 2. Background

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▶ (1) Values: ○ Agents’ rationalities are supported by mind-frames, that involve values and other constructs ○ These enable them to assess the state of the world and to decide on their actions. ○ Consequentalism. ▶ (2) Policy-making: ○ Choose means to achieve a better end state of the world. ○ Choices entail trade-offs (and different equilibria). ○ Choices depend on the values of policy-makers. ▶ (3) ABS: ○ Individual behaviour leads to emergent macro-behaviour.

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  • 2. Background

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▶ (4) Socio-cognitive technical systems ○ Agents: ■ Autonomous ■ Heterogeneous ■ Opaque ■ Socio-cognitive rationality ○ Social space: ■ Open regulated MAS ■ Situated ■ Shared state (admissible agent actions and events).

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  • 2. Background

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(SIMULATED) WORLD IMPLEMENTATION MODEL

[4]

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  • 2. Background

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META-MODEL

abstracts instantiates

PLATFORM

implements translates

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  • 3. Conceptual model

8 enable

POLICY SCHEMA

ENDS

INDICATORS

POLICY-MAKERS

define

POLICY-SUBJECTS

drive to influence

PARADIGMS

influence influence

POLICY DOMAIN CONTEXT

MEANS

INSTRUMENTS

PM

MIND FRAME

PM

MIND FRAME

PM

MIND FRAME

interact

PS

MIND FRAME

PS

MIND FRAME

PS

MIND FRAME

interact

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  • 3. Conceptual model

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▶ Simulation model:

POLICY DOMAIN CONTEXT

enable

POLICY SCHEMA

ENDS

INDICATORS

POLICY-SUBJECTS

drive to

MEANS

INSTRUMENTS

PS

MIND FRAME

PS

MIND FRAME

PS

MIND FRAME

interact

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  • 3. Conceptual model

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▶ Policy schema: ■ Policy means:

▸ They aim at producing behavioural changes on policy-subjects. ▸ Expressed as instruments (norms, incentives,...): ○ Afforded actions ○ Regulate actions ○ Persuade agents ■

Policy ends:

▸ They define desirable states intended to be achieved. ▸ Expressed as indicators to evaluate the evolving state of the world.

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  • 3. Conceptual model

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▶ Towards a metamodel for value-driven policy simulation: ■ Roles:

▸ Policy-makers (factions like government agencies, associations, NGOs,...) ▸ Policy-subjects (eg, farmer, farmer communities, RBA, utilities,...) ■

Information structures:

▸ State of the world ▸ Policy schema

  • Means (instruments)
  • Ends (indicators)

■ Subcontexts:

▸ Agenda setting ▸ Definition ▸ Negotiation ▸ Enactment ▸ Monitoring ▸ Domain language (to describe ) ▸ Action language ▸ Normative language

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  • 4. Examples

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CONTEXT

enable

POLICY SCHEMA

ENDS: farmers adopt the technology

(adoption rate)

POLICY-SUBJECTS

drive to

MEANS: modernisation incentivised with subsidies

FARMER

interact

PROFIT- DRIVEN

FARMER

PROFIT- DRIVEN

FARMER

PROFIT- DRIVEN

WATER USE IN AGRICULTURE

POLICY-MAKER VALUE: RURAL DEVELOPM ENT

define

SIMULATION

▶ Example # 1: Modernisation of farmers

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  • 4. Examples

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▶ Example # 1: Modernisation of farmers

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  • 4. Examples

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Adoption rate ▶ Example # 1: Evolving state of the world

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  • 4. Examples

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▶ Example #2: simplistic model with PM’s values interplay

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▶ Example #2: simplistic model with PM’s values interplay

  • 4. Examples

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CONTEXT

enable

POLICY SCHEMA

ENDS PM 1: wealth/area END PM2: groundwater/sust. POLICY-SUBJECTS

drive to

MEANS: water use constraints

FARMER

PRODUCTIVIST

WATER USE IN AGRICULTURE

POLICY-MAKER 1

VALUE:

RURAL DEVELOPMENT AND FARMER QUALITY LIFE

define

SIMULATION

POLICY-MAKER 2

VALUE:

ENVIRONMENT PROTECTION AND WATER SECURITY

FARMER

ENVIRONMENT ALIST

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  • 4. Examples

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▶ Example #2: simplistic model with PM’s values interplay Policy schema P1 Policy schema P2 Values

Rural development Farmer quality life Environmental protection Water security

Means:

  • SW use constraint (m3/ha)

2 500 2 500

  • GW use constraint (m3/ha)

3 500 1 000 Ends:

  • Indicators

Cultivated area (ha) Wealth (eur/hab) GW resources (hm3) GW Exploitation (%)

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  • 4. Examples

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▶ Example #2: simplistic model with PM’s values interplay Policy-subject 1 Policy-subject 2 Values

Autonomy Productivity Power Environmental protection Autonomy Fairness Efficiency

Actions

Withdraw1 Irrigate Sell Modernise1 Expand Withdraw2 Irrigate Sell Modernise2

Ends

Water Demand fulfilment Production Wealth Water Demand fulfilment Groundwater exploitation Neighbouring lawbreakers

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  • 4. Examples

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SLIDE 20
  • 4. Examples

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  • 5. Uses of the simulation

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▶ Basis for eliciting social values and ensuring value plurality.

POLICY MAKER1 ABMS POLICY MAKER2

POLICY SCHEMA A POLICY SCHEMA B

NEGOTIATION

POLICY SCHEMA C

ENACTMENT & MONITORING

Participatory modeling to build the tool (1) (2) Support definition

  • f a policy schema

(3) Support negotiation using the tool to define a consensual policy (4) monitoring the effects of the policy and compare with those of the simulation

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  • 6. Conclusions

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▶ Conclusions:

■ Understand the consequences of policies by making an explicit link between their values and the instruments and expected outcomes they choose. ■ Explore value-driven policies to see whether they are effective and good from a societal perspective [2,3]. ■ ABS is a useful tool to test policies, to deliberate and negotiate, and to monitor and verify the world state. ■ The policy simulation model can be reused as a policy design support system.

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Thank you

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▶ References:

■ [1] https://www.theguardian.com/commentisfree/2018/jul/08/observer-view-on-worldwide-scarcity-means-w e-should-conserve-water ■ [2] O'Brien, K. L. and Wolf, J. (2010), A values‐based approach to vulnerability and adaptation to climate

  • change. WIREs Clim Chg, 1: 232-242

■ [3] Perry, C.: ABCDE+F: A framework for thinking about water resources management. Water International 38(1), 95–107 (2013) ■ [4] Noriega, P., Padget, J., Verhagen, H., d’Inverno, M.: Towards a framework for socio-cognitive technical systems. In: Ghose, A., Oren, N., Telang, P., Thangarajah, J. (eds.) Coordination, Organizations, Institutions, and Norms in Agent Systems X. pp. 164–181. Lecture Notes in Computer Science 9372, Springer (2015)