Autonomous Intelligent Systems and (Un)Common Sense - 2026

 

Introduction

The aspiration to build machines capable of exhibiting intelligent behavior has accompanied the history of computing since its earliest days. During the 1960s, when I began working with computers, the notion of creating an electronic brain—what we would now describe as an Autonomous Intelligent System (AIS)—appeared more as a long-term vision than an achievable engineering objective.

Over the following decades, advances in computer science, control theory, artificial intelligence, robotics, machine learning, and most recently foundation models and generative AI have transformed that vision into practical reality. As a software engineer who has spent many years developing systems that today could be classified as intelligent, I take pride in having contributed, in a modest way, to this technological evolution.

An Autonomous Intelligent System continuously:

1. acquires information from its environment,
2. processes that information,
3. reasons about possible actions,
4. modifies its internal state, and
5. executes actions intended to achieve one or more objectives.

Each of these stages raises important scientific and engineering questions, including:

• the quality, reliability, and credibility of the information being processed;
• the representation of knowledge and uncertainty;
• decision making under incomplete information;
• learning and adaptation;
• explainability and transparency;
• safety and robustness;
• ethical and legal considerations.

Despite remarkable progress in artificial intelligence, two fundamental research problems remain largely unsolved:

1. transferring skills and knowledge efficiently between different tasks and domains, and
2. modelling common sense.

This document focuses exclusively on the second problem: the importance of incorporating common-sense reasoning into Autonomous Intelligent Systems.

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Fundamental Concepts

To establish a common terminology, consider that an Autonomous Intelligent System receives information from two broad categories of sensors.

Internal information describes the current state of the system itself. It originates from internal components and their interactions and is commonly associated with feedback, self-monitoring, diagnostics, or proprioception.

External information describes the system's relationship with its environment, including other systems, physical objects, human users, and external events. This information enables perception of the surrounding world.

Both categories are acquired through sensors, whether physical (e.g., cameras, LiDAR, microphones, temperature sensors) or virtual (e.g., software logs, databases, network traffic, APIs).

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Data Characteristics

Big Data is traditionally characterized by three fundamental properties (Laney, 2001):

1. Volume – the quantity of data generated.
2. Velocity – the speed at which data are produced and processed.
3. Variety – the diversity of data formats, sources, and modalities.

Later research expanded this framework by introducing additional characteristics, among which Veracity has become particularly relevant for artificial intelligence.

Many practical challenges associated with Volume, Velocity, and Variety can be addressed using established engineering techniques such as:

• selective sensing;
• intelligent sampling;
• data compression;
• feature extraction;
• dimensionality reduction;
• filtering and denoising;
• distributed processing.

However, these techniques alone cannot guarantee that the information being processed accurately represents reality.

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The Fourth V: Data Veracity

Among the extended Big Data characteristics, Veracity refers to the trustworthiness, quality, consistency, and reliability of data.

For intelligent systems, poor-quality information inevitably degrades decision quality. This observation follows directly from the well-known principle of Garbage In, Garbage Out (GIGO).

Consequently, an AIS should not only process incoming information but also continuously estimate its reliability.

Signal Processing provides a useful analogy. The Signal-to-Noise Ratio (SNR) measures how much useful information exists relative to unwanted disturbances. A high SNR generally improves the probability of accurate inference.

However, unlike classical communication systems, an intelligent agent cannot simply discard all apparent noise.

Unexpected or anomalous observations may correspond to:

• equipment failures,
• cyberattacks,
• environmental changes,
• sensor degradation,
• previously unknown situations.

Consequently, modern AI systems increasingly incorporate:

• anomaly detection,
• uncertainty estimation,
• probabilistic reasoning,
• Bayesian inference,
• confidence calibration,
• continual monitoring.

These mechanisms enable the system not only to identify expected situations but also to recognize when its own knowledge may be insufficient.

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Common Sense and Natural Language

Common sense has frequently been studied through the lens of natural language understanding because human knowledge is largely expressed linguistically.

Natural language reflects centuries of social evolution, shared experiences, cultural conventions, and implicit assumptions. Consequently, much common-sense knowledge is embedded within language itself.

Large Language Models have demonstrated impressive capabilities in capturing statistical regularities of language, and benchmark datasets such as CommonsenseQA, HellaSwag, PIQA, and Winograd have shown significant improvements over previous AI systems.

Nevertheless, recent research consistently shows that language modelling alone does not constitute genuine common-sense reasoning. Current models often fail when confronted with:

• novel situations,
• causal reasoning,
• physical reasoning,
• temporal consistency,
• long-term planning,
• counterfactual reasoning.

Moreover, restricting common sense exclusively to natural language unnecessarily narrows the problem.

Many Autonomous Intelligent Systems—industrial robots, autonomous vehicles, spacecraft, manufacturing systems, and sensor networks—interact successfully without using human language.

Communication among such systems relies on structured data, control signals, and shared protocols rather than linguistic expressions. Are these "other" common sense?

Therefore, a more fundamental question emerges:

Can an intelligent system exhibit common sense independently of natural language?

In other words: Is common sense fundamentally a linguistic phenomenon, or is it a broader capability involving perception, reasoning, prediction, adaptation, and decision making under uncertainty?

In my opinion, this question remains one of the central open problems in artificial intelligence research, and it extends beyond the scope of this document.

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References

Classical AI

• Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.) Pearson.
• Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460. 

Autonomous Intelligent Systems

• IEEE Standards Association. Ethically Aligned Design.

Big Data

• Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety.
• Thelwall, M., et al. (2021). "Garbage in, garbage out" revisited...

Common Sense

• Davis, E., & Marcus, G. (2015). Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence.
• Gary Marcus (2022). Deep Learning Is Hitting a Wall.
• John McCarthy (1959–2007). Programs with Common Sense
• CYC Project (Douglas Lenat) Cycorp (Cyc Project)
• CommonsenseQA Dataset. CommonsenseQA Paper 
• Winograd Schema Challenge. Winograd Schema Challenge
• HellaSwag Benchmark. HellaSwag Paper
• PIQA Benchmark. PIQA Paper

Foundation Models

• Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. 

Large Language Models

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• OpenAI Research / Model documentation: OpenAI Documentation

AI Safety

• Tabassi, E. (Ed.). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.

A Personal Reflection on Logistics, Quantum Computing, and Generative AI

In my view, Logistics is both an art and a science. It encompasses the technologies, methodologies, and decision-making processes that ensure the resources required to execute a process, event, or action are available at the right place, at the right time, and in the right quantity, while minimizing cost, time, and effort and maximizing efficiency, effectiveness, and resilience.

Today, the convergence of Quantum Computing and Generative Artificial Intelligence—including machine learning, deep learning, foundation models, and autonomous AI agents—offers an unprecedented opportunity to build and evaluate complex models that simulate dynamic systems with increasing accuracy while reducing computational cost, resource consumption, and development time.

Many of the world's most challenging problems involve complex adaptive systems operating far from equilibrium. Understanding their behavior requires the integration of multiple disciplines, including:

• Quantum Computing

• Artificial Intelligence and Machine Learning

• Game Theory

• Control Theory

• System Identification

• Operations Research

• Optimization

• Complex Systems Science

• Network Science

• Statistical Physics

Together, these disciplines provide complementary perspectives for modeling, analyzing, and optimizing systems whose behavior evolves continuously over time.

Potential applications include:

• Improved forecasting of earthquakes, including probability, location, magnitude, and timing.

• Modeling and forecasting price movements in financial markets.

• Optimizing urban traffic flow to reduce congestion and mitigate bottlenecks.

• Training recommendation systems with significantly fewer computational resources.

• Modeling armed conflicts to identify strategies that promote stable and sustainable peace.

• Optimizing the distribution of food, medicine, and emergency resources following natural disasters.

• Forecasting catastrophic scenarios associated with climate change to improve preparedness and resilience.

• Discovering hidden patterns in highly stochastic systems, including applications traditionally considered unpredictable.

At first glance, these challenges appear unrelated. However, they may share deeper mathematical structures.

This raises an intriguing research question:

Can models developed for one complex dynamic system be adapted, generalized, or transferred to another?

If seemingly different phenomena obey similar underlying principles, advances in one domain could accelerate discoveries in many others. Identifying these common structures could become one of the most exciting interdisciplinary research directions of the coming decades.

This idea motivates my current research interests, and I would be delighted to exchange ideas with researchers, engineers, scientists, and anyone passionate about complex systems.

If these topics resonate with you, I'd love to hear your thoughts. Please leave a comment or send me a message. Collaborative research often begins with a simple conversation.

A Tale of Game Theory: Metastable Peace

Introduction

A non-equilibrium system is like an inverted pendulum; it will not stay upright on its own. 

It requires:

1. Kahn Safety: Ensuring no player ever believes they have “First Strike Stability” (the ability to win without being destroyed).
2. Schelling Safety: Creating “Focal Points” for a new 3-way distribution of the hundreds of resources so no one feels the need to “Burn Bridges.”
3. Jervis Safety: Aggressive “De-escalation Signaling” to ensure that a defensive move by one isn’t seen as an existential threat by the other.

Kahn’s escalation ladder framework

Herman Kahn’s escalation ladder, introduced in his book On Escalation: Metaphors and Scenarios, is a seminal framework in strategic studies and game theory. It models the progression of a conflict from a minor disagreement to a full-scale "spasm" nuclear war.

As a computer scientist, you might view this as a state-transition model, where each rung represents a discrete state with increasing costs (negative payoffs) and decreasing reversibility.

Kahn identified 44 rungs, grouped into several distinct thresholds or "thresholds of no return." The ladder is designed to help strategists understand "escalation dominance"—the ability to increase the stakes to a level where the opponent cannot match the move and is forced to de-escalate or surrender.

Key Theoretical Concepts

1. Escalation Dominance: A player has escalation dominance if they can move to a higher rung where they have a relative advantage, while the opponent suffers more or lacks the capability to respond at that level. This forces the opponent to choose between a "disadvantageous peace" or an "unacceptable war."
2. The "Stability-Instability Paradox": Kahn’s framework suggests that stability at the highest rungs (fear of nuclear spasm) might actually encourage instability at the lower rungs (conventional skirmishes), as players believe the other side is too rational to escalate to the top of the ladder.
3. Bargaining through Risk: Kahn viewed escalation not as an accident, but as a negotiation process. Each rung is a "message." By climbing the ladder, a player is manipulating the "probability of disaster" to influence the opponent's cost-benefit analysis.

Schelling’s The Strategy of Conflict

Thomas Schelling’s "The Strategy of Conflict" fundamentally reoriented game theory from a purely mathematical exercise into a tool for analyzing social interaction and international relations. While Kahn focused on the structural rungs of escalation, Schelling focused on the psychological and informational dynamics of bargaining.

For a computer scientist, Schelling’s work can be viewed as the study of coordination protocols and constraint-based optimization in environments where communication is noisy or untrusted.

Key Concepts

A. Focal Points (Schelling Points)

Schelling observed that in the absence of communication, players can still coordinate their behavior by converging on a "focal point"—a solution that seems natural, special, or relevant to both.

*   Mechanism: Coordination is achieved through shared cultural or logical salience rather than explicit negotiation.

*   Example: If told to meet someone in New York City on a specific day without a time or location, most people choose "Noon at Grand Central Station."

B. The Threat that Leaves Something to Chance

Unlike Kahn’s linear ladder, Schelling viewed escalation as a "slippery slope." He argued that a threat is often most effective when it is not a 100% certainty, but rather a move that creates a probabilistic risk of a disaster that neither side can fully control.

*   Brinkmanship: This is the strategy of deliberately letting the situation get somewhat out of hand, forcing the opponent to back down to avoid the shared risk of a "fall."

Treatment of Signaling

In Schelling’s framework, talk is "cheap" unless it is backed by action or cost. Effective signaling requires Costly Signaling.

*   Signals as Information: A signal must change the receiver's belief about the sender's payoffs or future intentions.

*   The "Tripwire" Signal: Placing a small number of troops (e.g., in West Berlin during the Cold War) serves no tactical purpose in a full-scale invasion. However, it serves as a powerful signal because their deaths would *automatically* force a larger response, making the commitment to defend the territory credible.

The Strategy of Commitment

Schelling’s most counterintuitive insight was that weakness can be strength. In a bargaining situation, your power is often inversely proportional to your freedom of maneuver. If you can prove you cannot back down, the burden of avoiding a collision shifts entirely to your opponent.

A. The "Burning Bridges" Tactic

To make a commitment credible, a player must voluntarily and visibly destroy their own options for retreat. 

*   Logic: By removing the "Exit" node from your own decision tree, you force the opponent to choose between "Yield" or "Mutual Destruction." Since the opponent knows you have no choice but to stay the course, they are incentivized to yield.

B. Delegation and Automicity

Commitment is strengthened by removing human agency. 

*   The "Doomsday Machine": If a response is programmed to be automatic (an "if-then" statement triggered by an opponent's move), the threat becomes perfectly credible because the "threatener" no longer has the power to change their mind at the last second.

Schelling informs our treatment of signaling and commitment by highlighting that strategic behavior is the art of manipulating expectations. 

1.  Signaling is about overcoming the "cheap talk" problem through irreversible or costly actions.

2.  Commitment is about the paradox of choice: you gain bargaining power by constraining your future self, essentially "binding your hands" to make your threats or promises mathematically certain to the opponent.

Perception and misperception

Robert Jervis’s Perception and Misperception in International Politics introduced cognitive psychology to the game-theoretic models of international relations. While Kahn and Schelling assumed "rational actors" processing information correctly, Jervis demonstrated that **systematic cognitive biases** often cause leaders to misread signals, leading to unintended escalation or missed opportunities for cooperation.

From Computer Science, Jervis’s work can be viewed as an analysis of signal processing errors and noisy channel communication where the "receiver" has a biased prior that filters all incoming data.

The Core Thesis: The "Psychological Model"

Jervis argues that it is not the objective reality that determines a state's behavior, but the perceptions of reality held by decision-makers. He identifies several "cognitive shortcuts" that lead to sub-optimal outcomes.

A. The Impact of Pre-existing Beliefs (Bayesian Updating Failures)

Decision-makers are not good Bayesians. Instead of updating their beliefs based on new evidence, they engage in assimilation:

• New information is interpreted in a way that fits existing theories or "schemas."
• Evidence that contradicts a belief is often dismissed as "noise" or a "deception," while ambiguous evidence is seen as confirming the prior.

B. The "Common Deterrence" vs. "Spiral" Models

Jervis identified two primary frameworks through which leaders view conflict, and argued that choosing the wrong one is catastrophic:

1. The Deterrence Model: Assumes the opponent is an aggressor. The solution is to show strength (climb Kahn’s ladder). If you are wrong, you provoke the opponent unnecessarily.
2. The Spiral Model: Assumes the opponent is motivated by fear. The solution is to offer concessions to build trust. If you are wrong, you embolden a true aggressor (Appeasement).

Key Cognitive Biases in Signaling

Jervis identifies several specific "perceptual traps" that complicate the signaling theories of Schelling:

A. The Deterrence Bias (The "Sinister Attribution" Error)

States tend to see their own actions as a response to the environment (reactive), but view the opponent's actions as a reflection of their innate character or aggressive "programming" (dispositional). This is the Fundamental Attribution Error applied to geopolitics.

B. The Illusion of Transparency

Decision-makers often believe their own signals are clear and unambiguous. They assume that because they know they are peaceful, the opponent must also know it. When the opponent reacts defensively, the first state perceives this as unprovoked aggression rather than a fearful response to a misunderstood signal.

C. Centralization Bias

Observers tend to see the behavior of another state as more centralized and coordinated than it actually is. In reality, a "signal" might be the result of internal bureaucratic infighting or an accidental move by a local commander, but the receiver interprets it as a deliberate, high-level strategic "move."

Misperception in Commitment

Jervis challenges Schelling’s idea of "Burning Bridges." He notes that for a commitment to work, the opponent must perceive that the bridge is burned.

• If the opponent misses the signal due to cognitive "noise," the player has effectively committed themselves to a disastrous course of action that fails to deter.
• Over-reliance on "Salami Slicing": An opponent may misperceive a major commitment as a series of small, unconnected events, leading them to accidentally cross a "red line" they didn't realize existed.

Jervis’s work serves as a "debugging" manual for game theory. He suggests that to understand conflict, one must perform "Cognitive Mapping" of the opponent:

1. Identify their historical analogies: (e.g., Is the leader thinking about Munich 1938 or Vietnam 1965?)
2. Account for "Noise": Recognize that your own signals are likely being distorted by the opponent's internal politics.
3. Empathy as a Tool: Not as a moral virtue, but as a strategic necessity to see the world through the opponent's biased lens to predict their "irrational" moves.

Game with three players

To analyze a game with three main actors (Great Powers or "Oligarchs") and hundreds of limited resources (territories, frequencies, or compute nodes), we can synthesize Kahn, Schelling, and Jervis into a unified framework.

In this scenario, the game is no longer a simple zero-sum duel; it becomes a Coalition-Formation and Resource-Allocation Game with high dimensionality.

1. The Structural Layer: 

Kahn’s Multi-Front EscalationKahn’s ladder provides the State Space for the game. With three players (P1, P2, P3​) and hundreds of resources, escalation is not a single ladder but a Vector of Ladders.

Micro-Escalation: Instead of one "big" war, players can escalate on specific rungs for specific resources (e.g., P1​ and P2 are at Rung 15 regarding Resource A, but at Rung 2 regarding Resource B).

Threshold Management: With hundreds of resources, the "Nuclear Taboo" is replaced by "Systemic Stability Thresholds." If P1 seizes too many resources too quickly, they risk hitting a threshold that triggers a P2+P3 alliance.

Resource Saliency: Not all resources are equal. Kahn’s rungs help categorize which resources are "Counterforce" (strategic assets) vs. "Countervalue" (civilian/economic assets).

2. The Tactical Layer: 

Schelling’s Commitment and Coordination: Schelling provides the Transition Functions between states. In a 3-player game, the primary challenge is Triangular Bargaining.

Focal Points for Partitioning: With hundreds of resources, players cannot negotiate for each one individually. They will naturally gravitate toward "Schelling Points" for division (e.g., geographic boundaries, 33/33/33 splits, or historical ownership).

The "Burning Bridges" of Alliances: In a 3-player game, P1​ can gain leverage by "binding" themselves to P2. By making a public, irreversible commitment to defend P2​’s resources, P1 forces P3​ to back down or face a two-on-one conflict.

Salami Slicing at Scale: With hundreds of resources, a player can engage in "Salami Slicing"—taking one resource at a time. Each slice is too small to justify the opponent's jump up Kahn’s ladder, but the cumulative effect is a total takeover.

3. The Cognitive Layer: Jervis’s Signal Filtering

Jervis explains the Information Asymmetry and Noise in the system. As the number of resources and players increases, the probability of "Signal Overload" and "Misperception" grows exponentially.​​​​​​If P1 takes a resource from P2, P3 may misperceive this as a signal of P1’s global aggression, even if P1 intended it as a local correction. Jervis warns that P3 might "pre-emptively escalate" based on this misperception.​​​​

Attribution Errors in Resource Grabs: If P1 is losing resources to "market forces" or internal decay, P1 is likely to perceive this as a coordinated "shadow attack" by P2 and P3 (Centralization Bias).

The Spiral of Fear: In a 3-player resource game, any move to secure a resource for "defensive" reasons (to ensure P1​ has enough) is perceived by P2​ and P3​ as an "offensive" move to deprive them. This creates a Security Dilemma across hundreds of fronts.

A Unified Analytical Framework

To model this, we can define a Strategic State Vector for the system:

S={Ri,j, Ei,k, Ci,j}

Where:

Ri,j: The allocation of resource i to player j.

Ei,k​: The Kahn-level escalation state between players for resource k.

Ci,j: The Schelling-style commitment/alliance strength between players i and j.

The Simulation Logic:

Optimization (Schelling): Players attempt to maximize their R (resources) by creating C (commitments) that deter others. They look for Focal Points to minimize the cost of coordination.

Escalation (Kahn): If a player feels they are being "Salami Sliced," they jump to a higher E (rung) on a high-value resource to signal resolve.

The Error Term (Jervis): We introduce a "Perceptual Filter" Φ. Player j does not see S directly; they see Φj(S). If Φj perceives a defensive move as offensive, the player triggers an escalation E that the "Rational Model" would not predict.

Summary for the 3-Player, Multi-Resource Game:

• Use Kahn to define the "Cost of Conflict" at different intensities.
• Use Schelling to define how players "Lock In" their gains and form 2-vs-1 coalitions via credible signals.
• Use Jervis to audit the system for "Feedback Loops." In a 3-player game with many resources, the most likely cause of a "Spasm War" is not a rational calculation of resource value, but a misperceived signal where one player thinks the others have formed a secret coalition to eliminate them.

Strategic Advice: In this complex game, the winner is usually the player who successfully manages Jervis-style perceptions to keep the other two players from coordinating (Schelling) against them, while maintaining Escalation Dominance (Kahn) in the few resources that are truly "Focal."

Power Transition

To integrate Organski and Kugler’s Power Transition Theory (PTT) into the Kahn-Schelling-Jervis framework, we must shift from a static game to a dynamic, non-equilibrium system.

In PTT, the "winner" isn't necessarily the strongest player, but the one who manages the differential growth rates of the three actors. From CS, this is essentially a control theory problem where the "Rising Power" ($P_{rise}$) attempts to overtake the "Dominant Power" ($P_{dom}$)  before the system hits a terminal state (Kahn’s Rung 44).

 

1. The PTT Logic: The "Zone of Transition"

Organski and Kugler argue that war is most likely when a dissatisfied rising challenger approaches parity (80%–120% of the leader's power).

2. Explicit Reasoning: The Three-Actor Dynamics

In a three-player game ($P_{dom}$, $P_{rise}$, and $P_{dec}$—the declining power), the Power Transition creates specific strategic pressures:

A. The Preventive War Logic (Kahn + PTT)

The Dominant Power ($P_{dom}$) sees their lead shrinking.

• Reasoning: $P_{dom}$ calculates that it is cheaper to fight a "Limited Nuclear War" (Kahn Rungs 21-25) now while they still have escalation dominance, rather than waiting until $P_{rise}$ achieves parity.
• The Goal: Use a "demonstration of force" to break the $P_{rise}$ growth trajectory or force a resource-sharing agreement that favors the status quo.

B. The Challenger's "Salami Slicing" (Schelling + PTT)

The Rising Power ($P_{rise}$) knows they will be stronger tomorrow.

• Reasoning: Their optimal strategy is to avoid high-rung Kahn escalations until they reach parity. They use Schelling’s "Salami Slicing" to pick off the hundreds of limited resources one by one.
• The Commitment Problem: $P_{rise}$ cannot credibly commit (Schelling) to not revise the rules once they become #1. This "Insecurity Dilemma" makes $P_{dom}$ paranoid.

C. The Kingmaker Strategy (Jervis + PTT)

The Declining Power ($P_{dec}$) or the "Third Player" holds the balance.

• Reasoning: $P_{dec}$ knows they cannot win alone. They must signal (Schelling) a coalition with either $P_{dom}$ or $P_{rise}$.
• The Perceptual Trap (Jervis): $P_{dom}$ might misperceive $P_{dec}$'s neutrality as a secret alliance with $P_{rise}$. This misperception can trigger a "Pre-emptive Spasm" (Kahn Rung 44) because $P_{dom}$ feels "encircled."

3. Combining the Frameworks for the "Winner"

If a player’s goal is to maintain Kahn’s Escalation Dominance during a power transition, their reasoning follows these three scenarios:

Scenario 1: The "Hegemonic Squeeze" (Winner = $P_{dom}$)

To win, $P_{dom}$ must use Jervis’s insights to prevent a $P_{rise}+P_{dec}$ alliance.

• Action: $P_{dom}$ offers $P_{dec}$ a disproportionate share of the "hundreds of resources" to keep them satisfied.
• Escalation: $P_{dom}$ then uses Kahn’s Rungs to "draw a line in the sand" against $P_{rise}$ early. Because $P_{dom}$ still has superior tech/resources, they win the "Game of Chicken" (Schelling) because $P_{rise}$ isn't ready for a high-intensity conflict yet.

Scenario 2: The "Overtake by Stealth" (Winner = $P_{rise}$)

To win, $P_{rise}$ must manage Schelling’s focal points to keep the conflict "sub-threshold."

• Action: $P_{rise}$ frames every resource grab as "restoring historical norms" or "market competition" (Jervis-style framing) to avoid triggering $P_{dom}$’s preventive war instinct.
• Escalation: They only climb Kahn's ladder once their $dP\mathrm{/}dt$ has brought them to 110% of $P_{dom}$'s power. At that point, they possess Escalation Dominance by default.

Scenario 3: The "Spiral of Misperception" (Winner = None/System Failure)

This is the Thucydides Trap analyzed through Jervis.

• Reasoning: $P_{dom}$ sees $P_{rise}$’s growth as a threat. $P_{rise}$ sees $P_{dom}$’s military exercises as a prelude to an attack.
• Result: Both players climb Kahn’s ladder simultaneously, thinking they are "signaling resolve" (Schelling), but because of Jervis’s Illusion of Transparency, both sides believe they are being "defensive" while the other is "offensive." The transition ends in a Rung 44 "Spasm War."

Then, in a 3-player resource game, the "Winner" is the one who synchronizes their position on Kahn's ladder with their location in the PTT Zone of Transition.

• If you are rising, you stay at the bottom of the ladder and use Schelling-style coordination.
• If you are declining, you must use Jervis-style manipulation to force the other two into a conflict, or use Kahn-style escalation early while you still have the "Legacy Hardware" to win.

Is it possible to avoid a conflict?

In a dynamic, non-equilibrium system with three actors and shifting power levels (PTT), avoiding a "spasm" conflict (Kahn’s Rung 44) requires engineering the system to prevent the "Pre-emptive War" logic of the decliner and the "Salami Slicing" logic of the riser from intersecting.

From a systems and game-theoretic perspective, there are four primary "safety protocols" to keep such a system stable.

1. Transparency Protocols: Reducing the "Jervis Noise"

The greatest threat to a non-equilibrium system is Signal Distortion. If $P_{dom}$ perceives $P_{rise}$’s natural economic growth as a deliberate military "move," they may escalate prematurely.

• Verification Mechanisms: Implement high-fidelity, automated monitoring of the "hundreds of resources." If resource allocation is transparent (e.g., a shared ledger or "Open Skies" for compute/data), players cannot "Salami Slice" in secret.
• The "Hotline" (Direct Communication): Schelling argued that for a signal to be effective, it must be understood. Safety requires a "Low-Latency Metadata Channel" between actors to explain why a move was made, preventing the Centralization Bias (assuming every move is a top-down attack).

2. The "Golden Bridge": Schelling’s Exit Strategy

Sun Tzu and Schelling both emphasize that a trapped opponent is the most dangerous. If $P_{dom}$ (the declining power) feels their total elimination is inevitable, their rational move is a Kahn-style "Spasm" strike while they still have some hardware.

• Institutional Enmeshment: Create a "Focal Point" where $P_{dom}$ retains a prestigious, protected status even as their relative power drops.
• Side-Payments: Use the "hundreds of resources" to compensate the decliner. If $P_{dom}$ loses relative power but gains absolute resources, the incentive for a preventive war (Kahn) drops.

3. Interdependence: Raising the "Cost of Departure"

In a non-equilibrium system, safety is maintained if the payoff for "Defect" (War) is lower than the payoff for "Stay in the System" (Peace), even for the loser.

• Resource Entanglement: If the resources are "intertwined" (e.g., $P_1$ owns the raw materials, $P_2$ owns the processing, $P_3$ owns the distribution), a climb up Kahn’s ladder destroys the attacker’s own assets.
• The "Hostage" Strategy (Schelling): In ancient times, kings exchanged children. In a modern resource game, this means "Cross-Investment." If $P_{rise}$ has 30% of its assets located within $P_{dom}$’s territory, $P_{rise}$ cannot escalate without committing "Economic Suicide."

4. Managing the "Zone of Transition" (PTT Safety)

Organski and Kugler noted that the "danger zone" is the Parity Window (80%–120% power). Safety can be engineered by modifying the Velocity of Transition ($dP\mathrm{/}dt$):

• Damping the Growth Curve: If $P_{rise}$ grows at 10% per year, $P_{dom}$ panics. If $P_{rise}$ grows at 2% per year, $P_{dom}$ has time to psychologically and structurally adapt (reducing Jervis-style misperception).
• The "Third Player" as a Stabilizer: $P_{dec}$ (the third actor) can act as a Feedback Controller. By consistently siding with the "weaker" of the two giants, the third player keeps the system in a state of "Stagnant Parity," preventing anyone from reaching the threshold of Escalation Dominance.

Then: Is it possible to avoid conflict?

Yes, but it requires "Active Stabilizing Feedback."

Conflict is avoided when the Information Entropy of the system is low (everyone knows what the others are doing) and the Coupling is high (everyone's survival depends on the system's integrity). If you can keep the "Perceived Payoff" of the status quo higher than the "Maximum Potential Payoff" of a Kahn-style escalation, the system remains in a metastable peace.

References

1. Herman Kahn. On Escalation: Metaphors and Scenarios. Praeger, New York, 1965. Archive.org: https://archive.org/details/onescalationmeta0000kahn 

2. Thomas C. Schelling. The Strategy of Conflict. Harvard University Press, Cambridge, MA, 1960. Archive.org: https://archive.org/details/strategyofconfli00sche 

3. Thomas C. Schelling. Arms and Influence. Yale University Press, New Haven, CT, 1966 - 2008. Archive.org: https://archive.org/details/armsinfluence0000sche 

4. Robert Jervis. Perception and Misperception in International Politics. Princeton University Press, Princeton, NJ, 1976. Archive.org: https://archive.org/details/perceptionmisper0000jerv/page/n9/mode/2up 

5. A. F. K. Organski and Jacek Kugler. The War Ledger. University of Chicago Press, Chicago, 1980. 

GitHub:  

https://juanjosearanda.github.io/metastable/Metastable_Peace_Infographic.html 

https://juanjosearanda.github.io/metastable/Interactive_Metastable_Peace_Framework.html