OR-Responsibility Design Committee (OR-RDC)

1. Background and Mission Statement

1.1 Redefining Operations Research (OR)

Currently, many socially implemented OR (mathematical optimization) models are designed with the primary objective of "maximizing efficiency." However, in modern systems where complexity has increased (AI, advanced logistics, autonomous driving), the pursuit of optimization often leads to the "evaporation of responsibility," causing post-accident consensus-building costs to diverge to infinity.

The OR-Responsibility Design Committee (OR-RDC) redefines OR not merely as an "optimization tool," but as a "mathematical theory for formalizing and operating decision-making."

1.2 Mission

The mission of this committee is to identify the points where optimization collapses upon contact with the real world (the Unknowable Region) and to design and standardize "stop conditions," "intervention judgments," and "locus of responsibility" at those points—not as post-hoc ethics, but as a priori mathematical constraints (an extended OR model).

We do not engage in moral discussions about how AI or systems should be. Instead, we deal strictly with the structural boundaries of where a system must stop and who must take over.

1.3 Why OR-RDC is Necessary (Our Necessity)

The GhostDrift theory is not a proposal to attach "ethics" or "norms" from outside of OR. It is an extension to formalize the "breaking points"—uncertainty of responsibility, divergence of explanations, and deformation of objective functions due to post-hoc rationalization—that OR inevitably faces when implemented in society, and to treat them as internal constraints that finitely close the feasible region.

Therefore, the role of OR-RDC is not to add governance as a separate system, but to extend OR itself into a "truly completed OR" capable of enduring social implementation. An entity is required to undertake this extension, and thus, the OR-RDC is established.

1.4 Significance of Standardization

The evaporation of responsibility is not a moral failing of individual organizations, but a structural problem caused by the absence of standards. In an environment where responsibility boundaries, stop conditions, and intervention protocols are not standardized, unexplainability and consensus costs are externalized outside the organization, allowing short-term efficiency optimization to always prevail. As a result, a state where "no one takes responsibility" becomes the norm after accidents, and operational sustainability is lost.

What OR-RDC standardizes is not ethical norms, but technical standards to fix in advance the inevitable breaking points (divergence of explanation / uncertainty of responsibility / post-hoc rationalization) as common specifications (constraints, logs, evaluation metrics). In an environment where these standards are widespread, the room for short-term optimization to exist via externalization decreases, and systems converge to a form where they "stop when they should stop."

Specific examples such as the externalization of environmental burdens, AI runaway scenarios with responsibility vacuums, and the loss of trust/talent due to excessive growth are all isomorphic phenomena that occur when short-term optimization dominates in the absence of standards. This is a framework for receiving "warnings" from the natural environment, social systems, and AI operations—not as morality, but as operational design.

1.5 Theoretical Positioning

The GhostDrift theory proposed by this committee holds the following position relative to existing OR theories:

1. Domains NOT handled (or handles poorly) by existing OR:

   • Unknown Unknowns

   • Alteration of explanatory variables via Post-hoc Rationalization

   • The phenomenon where "responsibility disappears as explanation increases" (Divergence of Explanation Cost)

2. New Axes introduced by GhostDrift:

   • Unknowable Boundary

   • Post-hoc Impossibility

   • Responsibility Fixation

These are not "axes of optimization." They are the very conditions for the existence of decision-making. This theory lifts the implicit assumption that "uncertainty is formally definable in advance," which probabilistic optimization and robust optimization have premised. Instead of reducing or absorbing uncertainty, it provides a framework to restrict the degrees of freedom for post-hoc explanation by fixing unknowability before the decision.

In this sense, this theory is defined not as an optimization theory, but as an OR extension that provides the "pre-decision constraints" necessary for decision-making to be socially established.

2. Core Concepts & Definitions

The committee translates governance and responsibility theory into OR terminology (variables, constraints, functions) as follows:

Concept (GhostDrift Theory)	OR Definition (Mathematical Translation)	Implementation Meaning
Unknowable Boundary	Finite Closure of Feasible Region	The limit point where continuing optimization calculation causes post-hoc explanation costs to exceed benefits. It functions as a stop line meaning "do not calculate further" and as a closure prohibiting exploration outside the boundary (model extension, explanation addition, objective function modification).
Responsibility Fixation	Binding Agent ID to Decision Variables	Binding metadata of "whose judgment this is" inseparably to variables, making post-hoc transfer of responsibility impossible at the data structure level.
Post-hoc Impossibility	Constraint on Objective Function Mod.	A guarantee of temporal irreversibility preventing parameters from being readjusted after seeing the results with the excuse "it was rational at the time."
Explanation Cost	Penalty Term & Budget Constraint	Accounting for the complexity of accountability as a cost, ensuring "black box solutions that cannot be explained" are not selected. By setting an Explanation Budget Cap, it mathematically prevents decision latency caused by infinite excuses (divergence of explanation).

3. Scope of Activities

The committee will standardize "Responsibility Design" in the following three phases:

Phase 1: Boundary Design

• Activity: Standardizing "trigger conditions" where the system abandons autonomous optimization and delegates judgment to humans.

• Question: "At what percentage of delivery delay probability, or at what dimensionality of explanatory variables, must the AI stop judgment?"

• Question (Added): "In a multi-agent environment, how do we detect the exact moment when a state of Agent Ambiguity (where a single responsible subject cannot be determined) occurs?"

Phase 2: Protocol Design

• Activity: Formulating "Handshake" protocols when authority transfers from machine to human, or human to machine.

• Question: "During an emergency stop, what level of authority does field personnel need to intervene? How is that log preserved?"

Phase 3: Evaluation Design

• Activity: Defining new indicators (KPIs) to evaluate not only "efficiency" but also "safety at stop" and "clarity of responsibility."

• Question: "How do we audit and judge a model that is profitable but whose internal logic is unexplainable as 'Invalid'?"

4. Target Domains

(Declaration) The Target is General Decision-Making, Not Specific Industries

The target of OR-RDC is not limited to specific domains. What this committee standardizes is not individual applications like logistics, AI, or infrastructure, but the "Responsibility Boundary Design" itself, common to all systems operating decision-making under uncertainty. The domains shown below are merely initial instances, and it is premised that the committee's results will ripple out to all areas involving decision theory and operational design.

4.1 Logistics & Supply Chain

(Fixing responsibility during delays caused by weather or disasters. Applying Unknowable Boundary as consensus-building cost.)

4.2 AI Governance & Autonomous Agents

(Escalation structures and mathematical "low confidence" determination for hallucinations or runaways in LLMs, etc.)

4.3 Public Infrastructure & Energy

(Implementation of verifiable responsibility tracking logs in power supply adjustments, etc.)

4.4 Finance & Algorithmic Trading

(Standardization of mandatory stop rules when market environments fluctuate outside defined parameters.)

5. Academic Context & Related Work

GhostDrift theory connects to the lineage of the following OR precedents and positions itself as an extension theory complementing their "unresolved areas" (prevention of post-hoc rationalization and responsibility fixation).

5.1 Pre-fixing Uncertainty (Robust Optimization)

GhostDrift's approach of "fixing the unknowable region in advance to prevent post-hoc explanation proliferation" extends the "fixing of uncertainty sets" in Robust Optimization—a core trend in OR—to the layer of accountability.

• Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. Princeton University Press.

• Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1).

• Yanıkoğlu, İ., Gorissen, B. L., & den Hertog, D. (2019). A survey of adjustable robust optimization. European Journal of Operational Research.

5.2 Model Uncertainty & Defensive Decision (Distributionally Robust Optimization)

The concept of DRO, which secures the worst-case scenario under conditions where "it is unknown which model (interpretation) is correct," is mathematically consistent with GhostDrift's philosophy of preventing the post-hoc swapping of models to convenient ones after an accident.

• Delage, E., & Ye, Y. (2010). Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations Research, 58(3).

• Kuhn, D., Esfahani, P. M., Nguyen, V. A., & Shafieezadeh-Abdeh, S. (2025). Distributionally robust optimization. Acta Numerica.

5.3 Deep Uncertainty & Unpredictability (Decision Making under Deep Uncertainty)

Approaches like DMDU and RDM, which abandon the improvement of prediction accuracy and design decision-making processes based on the premise of unpredictability, are direct precedents for GhostDrift's "Unknowable Boundary."

• Marchau, V. A., Walker, W. E., Bloemen, P. J., & Popper, S. W. (2019). Decision Making under Deep Uncertainty: From Theory to Practice. Springer.

• Haasnoot, M., Kwakkel, J. H., Walker, W. E., & ter Maat, J. (2013). Dynamic adaptive policy pathways. Global Environmental Change.

5.4 Explainability & Control (XAIOR & Model Risk Management)

The "Responsibility Fixation" proposed by GhostDrift formulates the normative frameworks in recent Explainable OR (XAIOR) and implementation requirements in financial Model Risk Management (MRM) as mathematical constraints.

• De Bock, K. W., et al. (2024). Explainable AI for Operational Research: A defining normative framework (XAIOR). European Journal of Operational Research.

• Federal Reserve & OCC (2011). SR 11-7: Guidance on Model Risk Management.

• Bank of England / PRA (2023). SS1/23: Model risk management principles for banks.

6. Deliverables & Roadmap

Short-term (0-6 Months)

• GhostDrift Whitepaper v1.0: Publication of basic theory on "Responsibility Design as an Extension of OR."

• The Dictionary: Formulation of a list of mathematical definitions for "Unknowable," "Responsibility," and "Boundary."

Mid-term (6-12 Months)

• Implementation Guide: A manual for companies to introduce "Responsibility Constraints" into their own systems.

• Reference Implementation: Public demonstration of a GhostDrift-compliant model using logistics simulation.

Long-term (12+ Months)

• Certification Standard: A checklist for auditing whether companies or systems comply with "OR-RDC Standards."

7. Conclusion

Official Position Declaration

GhostDrift theory is an extension that mathematically provides the "conditions for decision-making to exist, prior to optimization" in Operations Research. This theory defines a new phase of OR that does not reduce uncertainty, but prevents the evaporation of responsibility by fixing unknowability.

Based on this theory, the OR-RDC standardizes technologies to safely encapsulate "Responsibility"—human society's last bastion—within the robust container of mathematical models. This marks the beginning of "truly completed OR" capable of enduring social implementation.