Now, we see the lens for what it truly is –
A powerful but flawed entity groping its way through an internal environment.
We probed the inner workings of the generic mind and familiarized ourselves with lenses in our previous post. We also learned from the mere existence of issues like the problem of foreign context that lenses can be less than perfect. Now it’s time to take this issue to the next level and call our attention to greater flaws lurking just beneath the surface. We will begin by casually observing a lens in its natural environment, and will eventually end by torturing the poor specimen with a battery of thought experiments.
The Internal Environment
What is the best word to describe an abstract space governed by a set of rules, capable of hosting entities that interact with one another according to these rules? A group? A field? A system? I tend to think of these as environments, no matter how abstract its rules or entities may be. Just as the Earth is an environment occupied by countless living organisms, the Internet can be thought of as an environment for data and programs, and perhaps the law can be seen as an environment filled with documents and historic cases. In much the same way, each generic sentient being contains a unique internal environment hosting a complex interaction between memories, emotions, and beliefs. Every observation, action and moment spent in deep thought will affect the relationship between the entities in this internal environment, even if the effects can be quite subtle. Conversely, the internal environment will strongly affect the judgments a generic will make and the actions he will take. In fact, the internal environment is probably just as important to a generic as the external environment that he lives in.
It’s possible to create environments that only contain mindless rule-abiding entities, but wouldn’t it be more interesting if the entities had a greater awareness of their environment? Generics are more than just passive objects in their external environment; they also use their senses and deductive skills to better understand that environment. They can make predictions about the future, and make informed decisions based on these predictions. Of course, a generic only can only learn about their environment through the small window that his senses provide, and his model of the environment can only be built based on what he knows1.
A direct parallel can be made with lenses. The lens occupies a central position in a generic’s internal environment, being involved directly or indirectly with almost everything there. It too can respond to changes in its environment using, among other things, a set of internal senses. Just like the external senses, the internal senses only provide a small window into the complex activity of the internal environment. Naturally, the lens can only make casual estimates and evaluations based on what it knows from its senses. It’s important to note that since all generics have a unique internal environment, the internal senses are completely private. A generic cannot sense anything about the internal environment of another generic, and even if he could he wouldn’t be able to make any sense2 out of the readings with respect to his own internal environment.
Why Evaluation
My previous post paid plenty of attention to the causal estimate, with a hard focus on its role in causal prediction and the problem of foreign context. It probably goes without saying that prior and posterior information are also quite important, but what about the evaluations? Even if we wave our hands and accept that evaluations are needed to give emotional content to the experience foundation, we still haven’t assigned any inherent meaning to the evaluations. What stops the lens from creating completely random evaluations?
Just like how the lens uses causal prediction to exercise the accuracy of its causal estimates, the lens uses value prediction to put its evaluations to the test. On the surface, value prediction works in the exact same way as causal prediction – the lens just uses an evaluation to make predictions rather than a causal estimate. However, this might sound like nonsense. Evaluations don’t capture cause-and-effect relationships in the physical world, so how can they be used to make any kind of prediction?
If you think about it, causal estimates relate the physical world to itself (even if the physical world includes interactions with other generics), whereas evaluations tie external events and actions to internal beliefs and emotions. In a sense, evaluations can represent cause-and-effect relationships between the external and internal environments. A generic can use value prediction to predict their future actions from their emotions or predict how an event will affect his beliefs about the world. In any case, information about the internal environment is collected using the internal senses, and can be made available as either prior or posterior information.
Because the internal senses cannot be shared, value predictions are distinctly self-centered. This doesn’t mean other generics can’t be involved though. When some generic A gives his coworker generic B an important task, A may predict that B will work hard and eventually finish the task based solely on a feeling of trust, even if he had no factual evidence to back this particular prediction3. Some generic C might be convinced that a group of authority figures will soon make a decision that he strongly disagrees with, simply because the group demonstrated very different moral beliefs on unrelated issues in the past. Generics sometimes assume that the internal environments of other generics are similar to their own, and perform value prediction on behalf of another generic. This is risky and almost always mistaken though. If generic A predicts that generic B will spend the whole day on the task because A himself felt very motivated at the time, then he is using value prediction and is quite likely to be wrong. This is not to be confused with the case where A has evidence to believe B too wanted the task done quickly, where he is using casual prediction and might not be wrong after all.
Other Uses of the Lens
There are some other tasks that the lens can do, though these capabilities are used less often than event interpretation, causal prediction or value prediction. For one, it can perform causal retrodiction and value retrodiction. Retrodiction is the opposite of prediction, where cause and effect are studied in opposite direction. Just as some effects can be deduced once one knows the causes, some causes can be deduced once one knows the effects. This can be used to learn more about an event once a generic learns more about its effect on the future, or to reconstruct older memories based on more recent memories.
The lens can also perform the more exotic task of event construction, where both prior and posterior information are created out of thin air to correspond to a given causal estimate and evaluation. This can be useful to create experiments that verify the accuracy of either the causal estimate or the evaluation. I suppose it can also be used to create cautionary tales to illustrate certain beliefs one has about the world, or fictional stories built around certain emotional states one had at the time.
Overgeneralization
The lens can indeed do many tasks, but it doesn’t always do them well. It definitely makes mistakes, and not just because the external and internal senses can’t present a perfect view of the external and internal environments. The lens eventually has to assume that the causal estimates and evaluations learned from the past will generalize to future scenarios, and sometimes it may be overly zealous in its generalization. This leads us to one of the most notable quirks of the lens – its tendency to overgeneralize4.
There can be several kinds of overgeneralization. In unconditional generalization, the lens tends to produce the same outputs regardless of the inputs it receives. For example, suppose generic A’s lens constantly produces evaluations related to dread or anxiety during event interpretation, no matter what events occur. Maybe A experienced many events in the past that really made him feel dreadful and anxious – so many that his lens learned to interpret all future events in a similar way. More subtly with pinning, the lens tends to produce a fixed output given a specific value for one of its inputs, regardless of the other inputs. A nice example here would be a lens performing causal prediction which always assumes that good intentions (causal estimate) lead to good outcomes (evaluation + posterior information), regardless of the situation (prior information). In this example, we say that the expectation of a good outcome is pinned to the assumption of good intention.
In all cases of overgeneralization, the lens learns a false pattern that doesn’t hold in general. What happens when a generic encounters evidence against his generalization? Let’s suppose that the lens in the first example learns from its internal senses that it feels excited after some event, which does not mesh well with the evaluation of the event as dreadful. On a lucky day, the lens will loosen itself and predict less dreadfulness for the future. But it also has plenty of ways to avoid the truth. It can try to find strange reasons to interpret the event as dreadful anyway, or it could treat this specific event as a rare exception to the rule and continue to overgeneralize. In the worst case, the generalization survives even after being challenged many times. What if two generalizations disagree on a single event? Let’s pit the first generalization in the previous paragraph against the second. If generic A learns that a well-meaning friend wants to help him on a task5, will he feel confident that the task will be done well, or will he feel anxious and dreadful? This example of dissonance is somewhat like applying an unstoppable force to an immovable object. If the two generalizations clash against one another often enough, one of them could disappear indefinitely. An even more bizarre result would be a modification to both generalizations which emphasizes the commonalities between the two. Perhaps the unstoppable force and the immovable object both survive the encounter with nothing more than a scratch, and generic A gets close to satisfying both generalizations by feeling grateful, but guilty and undeserving.
It’s probably near impossible to completely avoid overgeneralization, but it does seem like there are ways to mitigate its effects. One could try to develop a culture of open-mindedness that encourages generics to drop their generalizations in the face of counterexamples. Failing that, it is also possible to use dissonance to one’s advantage. One can set up scenarios that force a generic to reconcile two contradictory generalizations, which can either outright eliminate one of the generalizations, or convert both into forms that are easier to deal with. Then again, overgeneralization might not even be as bad of a problem as it seems; a more controversial point of view might even consider generalizations to be characteristic personality traits of a generic sentient being, with the assertion that forcing all generics to believe only in factually grounded statements robs them of their individuality. I partially agree with this sentiment – in my eyes, most cases of overgeneralization are just interesting quirks of a generic’s lens, not maladies that must be cured against all costs.
Footnotes
- Can one even describe an external environment without ultimately referencing sensory perception? Do the senses give qualitative experiences in a conscious mind, or are they more similar to sensor readings in a mechanistic system? Quite surprisingly, these questions are actually not all that relevant to this field; everything in this post applies no matter what answer you have to either question.
- Pun not intended.
- Why the feeling of trust then? It could have been earned through completely different means, such as by having friendly conversations in the past. It’s even possible that A happened to be in a good mood, allowing him to trust everyone at the moment.
- It should be noted that what counts as overgeneralization and what doesn’t is a matter of opinion. Still, there are many cases where it’s clear that a generic generalizes too far even after finding strong evidence against their beliefs. The opposite problem – undergeneralization – feels less like a mistake and more like some kind of sub-optimal use of knowledge.
- Assume that beyond all reasonable doubt, A knows that this friend has good intentions.




