Decision Governance

Decision governance is the set of values, principles, and practices that determine how an organization defines a decision situation, identifies and evaluates options, selects an option, implements the chosen course of action, and monitors its outcomes. It specifies who participates in each stage of the decision process, what information is required and how it is validated, which criteria guide comparison of options, how tradeoffs are managed, how accountability is assigned, and how learning from outcomes changes future decision processes. Decision governance provides a structure for agreeing on what constitutes decision quality in a specific organizational context and for maintaining that quality through systematic explanation, diagnosis of failures, design of new roles and procedures, simulation of likely outcomes, and continuous adjustment of decision rules as organizational conditions evolve.

Introduction to Decision Governance

• What is Decision Governance?
• What Is a High Quality Decision?
• When is Decision Governance Needed?
• When is Decision Governance Valuable?
• How Much Decision Governance Is Enough?
• Are Easy Options the Likely Choice?
• Can Decision Governance Be a Source of Competitive Advantage?
• How To Measure The Quality of Decision Governance?
• When To Delegate Decision Authority?

Stakeholders of Decision Governance 

• Who Is Responsible for Decision Governance in a Firm?
• Who are the Stakeholders of Decision Governance?
• What Interests Do Stakeholders Have in Decision Governance?
• What the Organizational Chart Says about Decision Governance

Foundations of Decision Governance and Relationship to Decision Making Models

• How to Spot Decisions in the Wild?
• When Is It Useful to Reify Decisions?
• Decision Governance Is Interdisciplinary
• Individual Decision-Making: Common Models in Economics
• Group Decision-Making: Common Models in Economics
• Individual Decision-Making: Common Models in Psychology
• Group Decision-Making: Common Models in Organizational Theory

Role of Explanations in the Design of Decision Governance

• Explaining Decisions
• Simple & Intuitive Models of Decision Explanations
• Max(Utility) from Variety & Taste
• Expected Uncertainty to Unexpected Utility
• Perceptiveness & Experience Shape Rapid Choices

Design of Decision Governance

• The Design Space for Decision Governance
• Decision Governance Concepts: Situations, Actions, Commitments and Decisions
• Decision Governance Concepts: Outcomes to Explanations
• Slow & Complex Decision Governance and Its Consequences
• Role of Decision Rights in Decision Governance
• Decision Responsibilities in Decision Governance

Design of Decision Governance: Psychological Factors

• Attention:
  • Attention Depends on Stimuli & Goals
  • Motivated Reasoning: How To Detect And Mitigate Its Risks
  • Attention → Memory: How Attention Influences Memory In Decision-Making
• Memory:
  • Selective Memory Can Be Desirable
  • Memory → Attention: How Memory Shapes Attention In Decision Making
• Mood:
  • Emotion ⇄ Mood: How They Interact And Why This Is Important For Decision Governance
• Emotions:
  • Emotions Mediate Decisions Always and Everywhere
  • Emotion → Attention: The Influence Of Emotions On Attention In Decision Making
  • Emotion ⇄ Mood: How They Interact And Why This Is Important For Decision Governance
• Commitment:
  • How To Enforce A Commitment?
• Temporal Distance:
  • Why Perception of Long Term Outcomes Should Be Influenced First?
• Social Distance:
  • Increased Social Distance (Over)Simplifies Explanations
• Expectations
  • Expectations: Different Definitions, Always About The Future
  • Expectations: How Do Expectations About Others Form?
  • Expectations: How They Form If Decision Makers Are Distant
• Uncertainty (TBD)
• Attitude:
  • Attitude: Role In Decision Making And Relationship To Emotion And Mood
• Values:
  • Values: General And Stable Guides For Decision Making
  • Values: How Values Influence Mood, Attitude, And Emotions In Decision Making
  • Values: How They Form And Change
  • Values: What Are Value Conflicts In Decision Making?
• Goals:
  • Goals: How Goals Shape Decisions
  • Goals: How Goals Form and Change
  • Goals: Factors That Stabilize and Destabilize Goals
  • Goals: Factors Influencing Goal Selection
  • Goals: How to Improve Alignment to a Team Goal
  • Goal Displacement: What It Is And How To Mitigate It
• Preferences:
  • Preferences: Why & How To Keep Preferences Stable
  • Preferences: Why & How To Destabilize Preferences
• Competence
  • Competence: Why And How Decision Quality Depends On Competence
  • Competence → Authority: More Is Usually More

Design of Decision Governance: Social Factors

• Impressions of Others:
  • Impressions Of Others: How They Influence Decisions And How To Regulate Them
• Reputation:
  • Reputation: Consequences Of High And Low Reputation On Decision Making
  • Reputation: Which Psychological Factors Influence It, And How
  • Reputation: Which Social Factors Influence It, And How
  • Reputation Mechanisms and Decision Governance
  • Reputation Mechanisms: Key Design Decisions
  • Reputation Mechanisms: Market vs. Non-Market Transactions
  • Reputation Mechanisms: Spot vs. Repeated Transactions
  • Reputation Mechanisms: Asset-Specific vs. Generic Transactions
  • Reputation Mechanisms: Information-Rich vs Information-Poor Transactions
  • Necessary Conditions for a Reputation Mechanism
  • Reputation Condenses Information. Which Other Mechanisms Have A Similar Role?
• Promises:
  • Promises: On Relationships Between Promises And Decisions
  • Promises: Using Promises To Evaluate Agent Reputation – Part 1
  • Promises: Using Promises To Evaluate Agent Reputation – Part 2
  • Promises: Using Promises To Evaluate Agent Reputation – Part 3
• Social Hierarchies:
  • Social Hierarchies: Why They Matter for Decision Governance
  • Social Hierarchies: Benefits and Limitations in Decision Processes
  • Social Hierarchies: How They Form and Change 
  • Power: Influence on Decision Making and Its Risks
  • Power: Relationship to Psychological Factors in Decision Making
  • Power: Sources of Legitimacy and Implications for Decision Authority
  • Power: Stability and Destabilization of Legitimacy
  • Power: What If High Decision Authority Is Combined With Low Power
  • Power: How Can Low Power Decision Makers Be Credible?
• Social Learning:
  • Social Learning: Impact on Decision Making Behavior
  • Social Learning: How to Accelerate It
  • Social Learning: How to Use It to Accelerate Adoption of Decision Governance
• Transparency:
  • Transparency: When Is More Better?
  • Transparency: Is Less Always Worse?
  • Transparency: What Influences It, And What It Influences In Turn
  • Unavoidable Frictions to Transparency
  • Specialization versus Transparency

Design of Decision Governance: Governance Factors

• Incentives:
  • Incentives in Decision Governance
  • Incentives: Components of Incentive Mechanisms
  • Incentives: Example of a Common Incentive Mechanism
  • Incentives: Building Out An Incentive Mechanism From Scratch
  • Incentives: Negative Consequences of Incentive Mechanisms
  • Crowding-Out Effect: The Wrong Incentives Erode the Right Motives
  • Crowding-In Effect: The Right Incentives Amplify the Right Motives
• Rules
  • Rules-in-use
  • Rules-in-form
• Transparency:
  • Transparency: When Is More Better?
  • Transparency: Is Less Always Worse?
  • Transparency: What Influences It, And What It Influences In Turn
• Level of detail:
  • Level of Detail Can Influence Probability Estimates

Change of Decision Governance

• How & Why Does Decision Governance Change?
• Public Policy and Decision Governance:
  • What is the Role of Public Policy in Decision Governance?
  • Dynamics of Public Policy Development
  • How Does Public Policy Influence Decision-Making?
• Compliance to Policies:
  • Adapting a Decision Process to Comply with a Policy
  • How a Decision Process Can Create Evidence of Compliance
• Transformation of Decision Governance
  • Punctuated Equilibrium: How to know if a Decision Process is ready for disruption
  • Policy Windows: What They Are And When They Occur
• Mechanisms for the Change of Decision Governance
  • Incrementalism: What it is, and when/how to implement it in decision governance
  • Governance Dynamics: Change Driven by Cases and Principles
  • Governance Dynamics: Case-Based Development of Decision Governance

Governance of Complex Decisions

• Complexity
  • Decision Complexity and Governance Responses
  • More Complex Decisions → More Decision Makers?
  • Understanding and Governing Decision Interdependence
• Many Decision Makers
  • More Decision Makers, Less Individual Accountability
  • Number of Decision Makers Influences Information Use
  • Decision Governance in Citizen Participation
  • When Is Credibility at Risk in Citizen Participation?
  • Decision Making at Scale: Differences between 1, 5, 20, and 100 Decision Makers
  • Decision Making at Scale: How Coordination and Communication Costs React to the Number of Decision Makers
  • Decision Making at Scale: How Number of Decision Makers Influences Decision Speed
  • Decision Making at Scale: How to Choose a Voting Procedure?
• Consensus
  • Consensus: What It Is and When to Seek It
  • Consensus: Common Elements of Consensus Seeking Procedures
  • What Helps or Hinders Consensus

AI Governance

AI governance is the set of values, principles, and practices that determine how an organization designs, deploys, monitors, and adapts decision processes that rely on artificial intelligence, including how it specifies the roles of humans and AI systems, defines the data and models that may be used, evaluates the reliability of outputs, assigns accountability for actions influenced by AI, and establishes procedures for ongoing oversight, risk assessment, and learning so that decisions made with AI remain aligned with organizational objectives and external constraints.

AI Governance: General Questions

• What is AI Governance for?
• Black Box Approach to AI Governance
• Can an Artificial Intelligence System Decide Autonomously?
• Business Risks of IP Compliance Requirements for Generative AI
• Private Data Use Consent as a Generative AI Compliance Requirement
• Does the EU AI Act apply to most software?
• When Is a Career Resilient to AI?
• Which Problems Is It Hard to Design AI for?
• Ambiguity of “Artificial Intelligence”

AI Governance and Authorship

• How to Make GenAI Better Faster? Authorship + Community + Credibility
• AI Growth through Expert Communities
• Why Specialized AI Should Be Certified by Expert Communities

AI Governance: Algorithmic Accountability Act of 2022

• Algorithmic Accountability Act of 2022 and AI Design
• Critical Decision Concept in the Algorithmic Accountability Act
• Algorithmic Accountability Act for AI Product Managers: Sections 1 and 2
• Algorithmic Accountability Act for AI Product Managers: Section 3
• Algorithmic Accountability Act for AI Product Managers: Section 4
• Algorithmic Accountability Act for AI Product Managers: Section 5
• Algorithmic Accountability Act for AI Product Managers: Sections 6 through 11
• Can an Artificial Intelligence Trained on Large-Scale Crawled Web Data Comply with the Algorithmic Accountability Act?

AI Governance: Role of Data Governance

• Data Authenticity, Accuracy, Objectivity, and Diversity Requirements in Generative AI
• AI Compliance at Scale via Embedded Data Governance
• Preconditions for a Market for High Quality AI Training Data
• What Does a Training Data Market Mean for Authors?
• Valuation of an AI Training Dataset

AI Governance: Explainability

• Limits of Explainability in AI Built Using Statistical Learning
• Opaque, Complex, Biased, and Unpredictable AI
• Can Opacity Be Solved in an AI Derived from an LLM?

Advice

• Analysis and Design of Advice (Book)
• What Is an Explanation?
• What Is Evidence?
• When (if ever) Is a Claim Objective?
• Nurtured Choice
• Advice and Requirements in a Decision Problem

Innovation & Ontology

• Define/Destroy: A Paradox to Accelerate Innovation
• Define/Destroy as a Method
• Define/Destroy a Business Services Marketplace
• Plastic Definitions
• Theories of Definition: Kant
• Theories of Definition: Belnap
• Theories of Definition: Crowds
• How Definitions Embed Past Choices
• How Definitions Influence Future Choices
• Conceptual Leaps and Definition Change
• How New Ideas Use Old Terms
• Unpacking Disagreement over New Ideas
• Linguistic Causes of Distracting Disagreement
• Plastic or Rigid Definition? Which to Prefer?
• Definition Networks and Their Purpose
• Hybrid Definition Networks and Their Role in Innovation

Requirements Engineering & Conceptual Modeling

• The Design of Requirements Modelling Languages (Book)
• Requirements Contracts: Definition, Design, and Analysis
• What Lies behind Requirements? Statement Grounds in Requirements Elicitation
• What If People Learn Requirements Over Time?
• What Happens to Intentional Concepts in Requirements Engineering if Intentional States Cannot Be Known?
• Monitoring in Business Intelligence Requirements Engineering
• Planning Optimal Agile Releases via Requirements Optimization
• AnalyticGraph: Next Generation Requirements Modeling and Reasoning Tools
• Requirements for Content Recommendation Systems
• Towards a General Formal Framework of Coherence Management in RE
• Requirements Problem and Solution Concepts for Adaptive Systems Engineering
• Topic Relevance in Requirements Elicitation
• How Stakeholders’ Commitment May Affect the Success of Requirements Elicitation
• Representation of Rules for Relevant Recommendations to Online Social Networks Users
• On Notification Importance for Online Social Network Users
• What Stakeholders Will or Will not Say: Topic Importance in Requirements Engineering Elicitation Interviews
• Agile Requirements Evolution via Paraconsistent Reasoning
• Legal Compliance of Roles and Requirements
• An Overview of Requirements Evolution
• The Requirements Problem for Adaptive Systems
• Knowledge-Based Recommendation Systems: A Survey
• An Exploratory Study of Topic Importance in Requirements Elicitation Interviews
• Context Factors and Requirements Problems
• Toward Benchmarks to Assess Advancement in Legal Requirements Modeling
• Adaptability of Process-based Service Compositions
• Choosing Compliance Solutions through Stakeholder Preferences
• Structure and Design of Business Analysis and Requirements Engineering Methods
• Product Portfolio Scope Optimization based on Features and Goals
• Influence of Context on Decision Making during Requirements Elicitation
• Context-driven Elicitation of Default Requirements
• Requirements Engineering Methods: A Classification Framework
• Finding Incremental Solutions for Evolving Requirements
• Towards Conceptual Foundations of Requirements Engineering for Services
• Requirements Engineering for Self-Adaptive Systems: Core Ontology and Problem Statement
• Establishing Information System Compliance via Argumentation
• Techne Family of Formalisms for the Resolution of Requirements Problems
• Requirements Oracles
• Mixed-Variable Requirements Roadmaps for Adaptive Systems
• Reasoning with Optional and Preferred Requirements
• Techne: Towards a New Generation of Requirements Modeling Languages
• Normative Management of Web Service Level Agreements
• Theory of Regulatory Compliance for Requirements Engineering
• From Decision Theory to Techne, and back
• Acceptability Condition for the Relative Validity of Requirements
• A Core Ontology for Requirements
• A Comprehensive Quality Model for Service-oriented Systems
• Dealing with Quality Tradeoffs during Service Selection
• Timing Nonfunctional Requirements
• Clear Justification of Modeling Decisions for Goal-oriented Requirements Engineering
• Context-Driven Autonomic Adaptation of Service Level Agreements
• Revisiting the Core Ontology and Problem in Requirements Engineering
• Engineering Pluripotent Information Systems
• Capturing and Using QoS Relationships to Improve Service Selection
• Continually Learning Optimal Allocations of Services to Tasks
• Clarifying Goal Models
• Tracing the Rationale Behind UML Model Change Through Argumentation
• Dynamic Web Service Composition within a Service-Oriented Architecture
• Achieving, Satisficing, and Excelling
• Dynamic Requirements Specification for Adaptable and Open Service-Oriented Systems
• Allocating Goals to Agent Roles During Multi-Agent Systems Requirements Engineering
• A More Expressive Softgoal Conceptualization for Quality Requirements Analysis
• Justifying Goal Models
• An Agent-Oriented Meta-model for Enterprise Modelling
• Formalizing Agent-Oriented Enterprise Models

Other Related Topics

• No Knowledge is Simple
• Building University Spin-Offs from Research on Decision-Making
• Value of Competence
• When Is a Design Problem a Sequence of Decision Problems?
• Speed vs Uncertainty
• Business Forecasts as Verifiable Explanations of Expected Value
• Economics of the Acceptability of an Argument
• The Firm as a Network of Teams
• Limits of Decentralized Autonomous Organizations (DAO)
• Alternative Incentives for Positive Network Effects
• The Paraconsistency Tax
• Specialization Costs in Functional Organization
• Choice in Absence of Utility and Probability Estimates