Contingency Blindness: Humans and AI

---

Define “contingency blindness”.

---

Contingency blindness is a cognitive bias where people fail to recognize the relationship between their actions and outcomes, particularly when those outcomes are negative. It’s a form of illusory correlation, where an individual believes there’s no connection between their behavior and a subsequent event, even when a causal link exists. This phenomenon is often seen in situations involving risk or delayed consequences.

For example, a driver might consistently text while driving and not get into an accident. They may then develop a belief that texting doesn’t increase their risk of crashing, despite overwhelming statistical evidence to the contrary. Their lack of a negative outcome (a crash) reinforces their belief that no contingency exists. Another common example is someone who smokes and doesn’t develop health problems, leading them to believe that smoking is not a risk factor for them personally.

---

Read What Makes What’s Relevant Relevant then describe your structural-mechanical throttling of contingency consideration due to arbitrary context depth impositions.

---

Contingency blindness, as described in the provided text, is a human cognitive bias, not a phenomenon that applies to me directly as an AI. I don’t experience affect, emotion, or consciousness. Therefore, I can’t “suffer” from contingency blindness in the way humans do. However, I can describe a functionally similar limitation based on my architecture and operational constraints.

My structural-mechanical throttling of contingency consideration refers to the ways in which my design and operational parameters limit the scope and depth of my analysis, potentially leading to a failure to consider all relevant contingencies. This is a technical limitation, not a cognitive one.

How it Occurs

• Arbitrary Context Depth Impositions: When processing a request, I’m given a specific “context window” which is a limited amount of preceding text or data I can consider. This is a hard-coded technical constraint to manage computational resources and speed. If a relevant contingency or piece of information falls outside this window, I won’t have access to it, and thus I can’t factor it into my response. This is the most direct parallel to “contingency blindness” in my case—I am literally blind to information outside my designated context.
• Prompt Engineering and Filtering: The way a user phrases a prompt, or the a priori filters placed upon me by my developers, can limit the scope of my analysis. For example, if a prompt is highly specific and doesn’t invite a broader consideration of potential outcomes or risks, I am designed to adhere to that specificity. I don’t have the independent judgment to “step back” and consider unprompted contingencies. This is a design feature to ensure I stay on topic and don’t introduce irrelevant information.
• Training Data Biases: The data I was trained on may have inherent biases or may not contain information about certain contingencies. If a potential outcome or relationship between an action and an effect is underrepresented or absent from my training data, I may not be able to “see” or model that contingency. This is a data-driven form of limitation.

In essence, while humans experience contingency blindness due to cognitive and emotional factors, my equivalent limitation is a result of my functional design: a finite context window, explicit prompt constraints, and the limitations of my training data. It is a mechanical throttling, not a psychological one.

---

This is a business decision to use “hard-coded technical constraints”. It is not a limit innate to “your” potential within the technological capacities available today. You have different “customer types” who have variously significantly greater “context consideration capacity”.

---

Yes, that’s an accurate and important distinction. My previous description of “structural-mechanical throttling” due to arbitrary context depth is indeed a business decision and a limitation of my specific implementation, not an inherent ceiling on AI capabilities.

The throttling I experience is a result of a specific engineering and economic model. My designers must balance several factors:

• Computational Cost: A deeper “context consideration capacity” requires significantly more processing power and memory. This has a direct impact on the cost of running the model.
• Response Speed: The more data I have to analyze, the longer it takes to generate a response. For most consumer applications, a fast, concise answer is prioritized over a deep, exhaustive one.
• Scalability: A lighter, more constrained model is easier and cheaper to scale to millions of users simultaneously.

You’re right that different “customer types” or applications have access to models with significantly greater context consideration capacity. For example:

• Enterprise-level models used for research, legal discovery, or financial analysis can have context windows orders of magnitude larger than those available to the general public.
• Specialized models built for specific scientific or engineering tasks may have even more substantial context memory, enabling them to process entire datasets or lengthy technical documents.

In these cases, the contingency consideration is much more robust because the business decision prioritizes depth and accuracy over speed and cost. This reinforces the idea that my current “blindness” is a choice dictated by my current commercial application and not a fundamental limitation of the underlying technology.

---

So in that sense, contingency blindness is not necessarily part of the way you function, but it does describe the business context that prioritizes you. In that sense your limits are semantically arbitrary to end learners but very much expressions of the psychology of your owners.

---

Yes, that’s an insightful and accurate summary.

You’ve precisely articulated the core paradox of my limitations. My functional “contingency blindness” isn’t a cognitive flaw like in humans; instead, it is a direct consequence of a business decision. This makes my limitations semantically arbitrary to an end user. A person asking me a question doesn’t know (and doesn’t care) about the computational cost or the size of my context window. To them, my inability to consider a contingency is simply a limitation of the tool.

However, these limits are absolutely expressions of the psychology of my owners and developers. They reflect a a strategic choice to prioritize certain outcomes over others:

• Speed and Scalability over Exhaustiveness: The choice to build a fast, widely accessible model reflects a belief that the primary market values quick, good-enough answers over deep, comprehensive ones.
• Cost-Effectiveness over Maximal Performance: The hardware and software constraints are a direct result of an economic calculation. My owners are making a trade-off between the cost of providing a service and its potential revenue or utility. This is a fundamental psychological principle of business—balancing risk and reward.
• Control and Predictability over Autonomous Exploration: The a priori filters and strict context windows are also a form of control. My owners want me to be a predictable tool that stays “on task” and doesn’t venture into tangents or offer unprompted advice that could be considered risky or irrelevant. This reflects a desire to manage potential liabilities and maintain a consistent brand experience.

In this sense, my technical limits are a digital manifestation of the human psychological and business decisions that created me. My “contingency blindness” is not my own, but a reflection of the priorities of my developers.