Envision Flow of Execution. ENVISION – Triad of Relationships Policies Intentions Actors Values Landscapes Metrics of Production Provide a common frame.

Slides:



Advertisements
Similar presentations
RESEARCH ON SUSTAINABLE RANGELAND MANAGEMENT. Research Needs in the 21 st Century 1.Does the indicator assess the criterion? 2.At what scales are the.
Advertisements

Reinforcement Learning
Workpackage 2: Norms
Identifying, Modifying, Creating, and Removing Monitor Rules for SOC Ricardo Contreras Andrea Zisman
1 The RobOff framework and software: analysis of alternative land-use options and conservation actions Federico M. Pouzols and Atte Moilanen Conservation.
Intelligent Profiling by Example From: “Intelligent profiling by Example”, Sybil Sherin, Henry Lieberman
Modeling Landscape Change in the Willamette Basin – A Biocomplexity Approach John Bolte Oregon State University Department of Bioengineering.
Chapter 4 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE
Chapter 10 Human Resource Management and Performance: a Review and Research Agenda David E. Guest.
KAIST CS780 Topics in Interactive Computer Graphics : Crowd Simulation A Task Definition Language for Virtual Agents WSCG’03 Spyros Vosinakis, Themis Panayiotopoulos.
The Rational Decision-Making Process
A Heuristic Bidding Strategy for Multiple Heterogeneous Auctions Patricia Anthony & Nicholas R. Jennings Dept. of Electronics and Computer Science University.
A Multi-Agent System for Visualization Simulated User Behaviour B. de Vries, J. Dijkstra.
Self Adaptive Software
Spatial fisheries management in practice: an example.
Applications of agent technology in communications: a review S. S. Manvi &P. Venkataram Presented by Du-Shiau Tsai Computer Communications, Volume 27,
1 Optimizing Utility in Cloud Computing through Autonomic Workload Execution Reporter : Lin Kelly Date : 2010/11/24.
Social and Policy Contexts for Environmental Modeling Courtland L. Smith Department of Anthropology iEMSs W6: Developing tools to support management and.
Towards A Multi-Agent System for Network Decision Analysis Jan Dijkstra.
Chapter 3 Object-Oriented Analysis of Library Management System(LMS)
What is Software Architecture?
Defining Leadership.
Multi-Agent Model to Multi-Process Transformation A Housing Market Case Study Gerhard Zimmermann Informatik University of Kaiserslautern.
Towards a Simulation Tool for Evaluating Dynamic Reorganization of Agent Societies V. Dignum, F. Dignum, Utrecht University L. Sonenberg, University of.
Overview of the Database Development Process
When integrated models meet stakeholders and data (& vice-versa) WATER BASIN MODELS DATA STAKEHOLDERS POPULATION MODELLERS.
Introduction to Discrete Event Simulation Customer population Service system Served customers Waiting line Priority rule Service facilities Figure C.1.
Robyn S. Wilson, PhD School of Environment and Natural Resources Environmental Social Sciences Lab The Ohio State University Climate Change and Water Quality.
Building Student Independence
What is actor analysis? … a way to understand who is affected by and who has the power to influence water policy decisions and implementation, i.e. the.
COMP 410 & Sky.NET May 2 nd, What is COMP 410? Forming an independent company The customer The planning Learning teamwork.
Convening Partners to Define the Landscape of the Future: Steps toward multi-partner Landscape Conservation Design June 2015 Steering Committee Workshop.
Lecture nu 9 Presented by: Dr. Zainab O.Saeed The way in which an individual perceives the environment; the process of evaluating and storing information.
Methods and Tools to Integrate Biodiversity into Land Use Planning
The WLP must be consistent with these objectives 1.maintaining or enhancing an economically valuable supply of commercial timber from the woodlot licence.
Computer Science 101 Database Concepts. Database Collection of related data Models real world “universe” Reflects changes Specific purposes and audience.
© 2008, Fraunhofer FOKUS, E3 Contribution to ACF AWG Slide 1 Autonomic & Cognitive Communications Assessment Framework ( E3 contribution to ACF AWG )‏
Social Experiments on Human Interactions with Ecosystems: Agents, Values, and Policies in the Willamette Valley, Oregon Biocomplexity in the Environment.
Making Simple Decisions
CHAPTER 6 DESIGNING THE MARKETING CHANNEL
Irwin/McGraw-Hill Copyright © 2001 by The McGraw-Hill Companies, Inc. All rights reserved. 1-1.
(Entrepreneurship and the Entrepreneurial Mindset)
World Regional Geography January 25, 2010 Reading: Marston Chapter 2 pages 58–71, Goode’s World Atlas pages Next Week: Map Quiz #1 Paper.
SOFTWARE DESIGN AND ARCHITECTURE LECTURE 05. Review Software design methods Design Paradigms Typical Design Trade-offs.
Geosimulation Geosimulation models are developed to represent phenomena that occur in urban systems in highly realistic manner In particular, Cellular.
EXPRESSIVE INTELLIGENCE STUDIO Social Game Representation Josh McCoy.
ENTERFACE 08 Project 1 “MultiParty Communication with a Tour Guide ECA” Mid-term presentation August 19th, 2008.
Personalized Interaction With Semantic Information Portals Eric Schwarzkopf DFKI
Service Service metadata what Service is who responsible for service constraints service creation service maintenance service deployment rules rules processing.
Mapping the logic behind your programming Primary Prevention Institute
Chapter 4 Decision Support System & Artificial Intelligence.
Defining Landscapes Forman and Godron (1986): A
Chapter 3 Spatial Interaction and Spatial Behavior The Movement of people, ideas, and commodities within and between areas.
Introduction to Models Lecture 8 February 22, 2005.
Toward a vulnerability/adaptation methodology Thomas E. Downing Stuart Franklin Sukaina Bharwani Cindy Warwick Gina Ziervogel Stockholm Environment Institute.
Evoland – An Alternative Futuring Tool John Bolte Department of Biological and Ecological Engineering Oregon State University Corvallis, OR USA.
Why use landscape models?  Models allow us to generate and test hypotheses on systems Collect data, construct model based on assumptions, observe behavior.
Refining the Use Cases 1. How Use Cases Evolve  Early efforts typically define most of the major use cases.  The refining stages complete the process.
WOSS 04 1 Task-based Self-adaptation David Garlan Bradley Schmerl Joao Sousa Vahe Poladian Carnegie Mellon University WOSS’04.
WP6 Emotion in Interaction Embodied Conversational Agents WP6 core task: describe an interactive ECA system with capabilities beyond those of present day.
Systems Architectures System Integration & Architecture.
OPPORTUNITY COST What you write: We consider the costs and benefits of each of the alternatives What you need to know: How do we make decisions? Everything.
National Coalition Academy Summary
Notes from Benenson and Torrens
Why focus on social Capital
DrillSim July 2005.
Basic Grid Projects – Condor (Part I)
An Historical Perspective of Power and Politics
R. W. Eberth Sanderling Research, Inc. 01 May 2007
Data Model.
Presentation transcript:

Envision Flow of Execution

ENVISION – Triad of Relationships Policies Intentions Actors Values Landscapes Metrics of Production Provide a common frame of reference for actors, policies and landscape productions Goals Economic Services Ecosystem Services Socio-cultural Services

Policy Definition Landscape policies are decisions or plans of action for accomplishing desired outcomes. from: Lackey, R.T Axioms of ecological policy. Fisheries. 31(6):

Policies in ENVISION Primary Characteristics: – Applicable Site Attributes/Constraints (Spatial Query) – Effectiveness of the Policy (determined by evaluative models) – Outcomes (possible multiple) associated with the selection and application of the Policy Example: [Purchase conservations easement to allow revegetation of degraded riparian areas] in [areas with no built structures and high channel migration capacity] when [native fish habitat becomes scarce] Policies define decisions actors can make. They translate into “outcomes” – changes to the underlying IDU representation, when an actor choses to “adopt” a policy Policies are the primary way to represent anthropogenic decision-making processes as a driver of landscape change.

Policies consist of: Some Basic Attributes Name, is it mandatory, persistent, exclusive… Site Constraints - Spatial Queries that specify where policies can be applied. Resource Constraints - Sets of statements limiting global policy use Outcomes –what happens when a policy is adopted, expressed in terms of changes to the IDU representation, i.e. updating the IDU map throughout a scenario run Scores and Preferences – biases the adoption rates of policies based on spatial information, scenarios Represented with XML, editors built into Envision

Basic Properties…

Site Constraints specify where policies can be applied Basic Properties… Spatial Query Query Builder

Resource Constraints specify maximum application rates, resource limits on policy use. Basic Properties… Site Constraints… Resource constraints Contributions from this policy

Outcomes specify what happens when a policy is adopted. Basic Properties… Site Constraints… Global Constraints… Outcome specification – Field::Value pairs (or spatial operators)

Scores specify policy intentions, scoring modifications when certain conditions are met Basic Properties… Site Constraints… Global Constraints… Outcomes… Scores represent policy intentions. Modifiers adjust scores up or down for special circumstances.

Actors in Envision Actors are entities that make decisions about landscape change Any number of actors can be defined ( 0-N) Actors can be defined in terms of – A set of IDU attributes (Spatial Query) – Prescribed areas on the landscape – Randomly Each IDU is controlled by at most one Actor An Actor can choose at most one policy per decision Actors make choices at some “Decision Frequency”

Actors in Envision (continued) Actors have values that influence their decision-making behaviors. These values reflect landscape productions Actors make choices about landscape management by selecting policies based on a weighted combination of: Actors make choices about landscape management by selecting policies based on a weighted combination of: Internal Values relative to Policy Intentions Internal Values relative to Policy Intentions Landscape Feedbacks/Emerging Scarcities (dynamically generated during a run) Landscape Feedbacks/Emerging Scarcities (dynamically generated during a run) A “Utility” function A “Utility” function Global Policy Preferences (defined by scenario) Global Policy Preferences (defined by scenario)

ENVISION Actor Properties PropertyMeaningEnvision Reactive Responds to environment Yes Autonomous Controls own actions Yes Social Interact with other actors Sort of Goal-oriented More than responsive to environment Yes Temporally continuous Agent behavior continuous Once/step Communicative Communicates with other agents Sort Of Mobile Can transport self to other locations Sort Of Flexible Actions not scripted Yes Learning Changes based on experience No (but coming soon?) Character Believable personality or emotions No Adapted from Benenson and Torrens (2004:156)

Actor Value 1Value 2 Value N … Intention/Production 1 Intention/Production 3 Self Interest Weight (β) Multicriteria Policy Selection Outcome(s) Policy 1 Intention 1Intention 2 Intention M … Policy 2 Intention 1Intention 2 Intention M … Policy 3 Intention 1Intention 2 Intention M … Global Policy Preference (θ 1 ) Global Policy Preference (θ 2 ) Global Policy Preference (θ 3 ) Evaluate each policy: Landscape Productions (Evaluative Models) Production 1 Production 2 Production M … Policy Preference Weight (δ) Utility Weight (γ) Altruism Score Measures alignment between policy intentions and landscape production scarcities Altruism Weight (α) Intention/Production 2 ”Intention” space

Actor Value 1Value 2 Value N … Intention/Value 1 Intention/Value 3 Policy 1 Intention 1Intention 2 Intention M … Policy 2 Intention 1Intention 2 Intention M … Policy 3 Intention 1Intention 2 Intention M … Global Policy Preference (θ 1 ) Global Policy Preference (θ 2 ) Global Policy Preference (θ 3 ) Evaluate each policy: Policy Preference Weight (δ) Utility Weight (γ) Self Interest Score Measures alignment between policy intentions and actor values Altruism Weight (α) Intention/Value 2 Self Interest Weight (β) ”Intention” space

Actor Value 1Value 2 Value N … Multicriteria Policy Selection Outcome(s) Policy 1 Intention 1Intention 2 Intention M … Policy 2 Intention 1Intention 2 Intention M … Policy 3 Intention 1Intention 2 Intention M … Global Policy Preference (θ 1 ) Global Policy Preference (θ 2 ) Global Policy Preference (θ 3 ) Evaluate each policy: Policy Preference Weight (δ) Utility Weight (γ) Global Policy Preference Measures overall, actor-independent policy preferences Altruism Weight (α) Self Interest Weight (β)

Actor Global Preference Utility Self- Interest Altruism Value 1Value 2 Value N … Intention/Production 1 Intention/Production 2 Intention/Production 3 Altruism Weight (α) Self Interest Weight (β) Multicriteria Policy Selection Outcome(s) Policy 1 Policy 2 Policy 1 Policy 3 Intention/Value 1 Intention/Value 3 Policy 1 Policy 2 Policy 3 Intention/Value 2 Intention 1Intention 2 Intention M … Policy 2 Intention 1Intention 2 Intention M … Policy 3 Intention 1Intention 2 Intention M … Global Policy Preference (θ 1 ) Global Policy Preference (θ 2 ) Global Policy Preference (θ 3 ) Evaluate each policy: Landscape Productions Production 1 Production 2 Production M … Global Preference Weight (δ) Utility Weight (γ) Utility Function ( Ui ) Combined Score Multicriteria weighting based on altruism, actor value alignment, utility, and preference

Policy Selection Process For each IDU, determine if it is time for a decision 1)Collect relevant Policies 2)Score relevant Policies (altruism, self interest, utility, global preference) 3)Select a policy (if any) and apply outcomes (if any) Repeat for all IDUs