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An explanation is a set of statements usually constructed to describe a set of facts which clarifies the causes, context, and consequences of those facts. This description of the facts et cetera may establish rules or laws, and may clarify the existing rules or laws in relation to any objects, or phenomena examined. The components of an explanation can be implicit, and interwoven with one another.
An explanation is often underpinned by an understanding or norm that can be represented by different media such as music, text, and graphics. Thus, an explanation is subjected to interpretation, and discussion.
In scientific research, explanation is one of several purposes for empirical research. Explanation is a way to uncover new knowledge, and to report relationships among different aspects of studied phenomena. Explanation attempts to answer the "why" and "how" questions. Explanations have varied explanatory power. The formal hypothesis is the theoretical tool used to verify explanation in empirical research.
While arguments attempt to show that something is, will be, or should be the case, explanations try to show why or how something is or will be. If Fred and Joe address the issue of whether or not Fred's cat has fleas, Joe may state: "Fred, your cat has fleas. Observe the cat is scratching right now." Joe has made an argument that the cat has fleas. However, if Fred and Joe agree on the fact that the cat has fleas, they may further question why this is so and put forth an explanation: "The reason the cat has fleas is that the weather has been damp." The difference is that the attempt is not to settle whether or not some claim is true, but to show why it is true.
In this sense, arguments aim to contribute knowledge, whereas explanations aim to contribute understanding.
Justification is the reason why someone properly holds a belief, the explanation as to why the belief is a true one, or an account of how one knows what one knows. In much the same way arguments and explanations may be confused with each other, so too may explanations and justifications. Statements which are justifications of some action take the form of arguments. For example, attempts to justify a theft usually explain the motives (e.g., to feed a starving family).
It is important to be aware when an explanation is not a justification. A criminal profiler may offer an explanation of a suspect's behavior (e.g.; the person lost their job, the person got evicted, etc.). Such statements may help us understand why the person committed the crime, however an uncritical listener may believe the speaker is trying to gain sympathy for the person and his or her actions. It does not follow that a person proposing an explanation has any sympathy for the views or actions being explained. This is an important distinction because we need to be able to understand and explain terrible events and behavior in attempting to discourage or prevent them.
There are many and varied events, objects, and facts which require explanation. So too, there are many different types of explanation. Aristotle recognized at least four types of explanation. Other types of explanation are Deductive-nomological, Functional, Historical, Psychological, Reductive, Teleological, Methodological explanations.
The notion of meta-explanation is important in behavioral scenarios that involve conflicting agents. In these scenarios, implicit or explicit conflict can be caused by contradictory agents' interests, as communicated in their explanations for why they behaved in a particular way, by a lack of knowledge of the situation, or by a mixture of explanations of multiple factors. In many cases to assess the plausibility of explanations, one must analyze two following components and their interrelations: (1) explanation at the actual object level (explanation itself) and (2) explanation at the higher level (meta-explanation). Comparative analysis of the roles of both is conducted to assess the plausibility of how agents explain the scenarios of their interactions. Object-level explanation assesses the plausibility of individual claims by using a traditional approach to handle argumentative structure of a dialog. Meta-explanation links the structure of a current scenario with that of previously learned scenarios of multi-agent interaction. The scenario structure includes agents' communicative actions and argumentation defeat relations between the subjects of these actions. The data for both object-level and meta-explanation can be visually specified, and a plausibility of how agent behavior in a scenario can be visually explained. Meta-explanation in the form of machine learning of scenario structure can be augmented by conventional explanation by finding arguments in the form of defeasibility analysis of individual claims, to increase the accuracy of plausibility assessment.
A ratio between object-level and meta-explanation can be defined as the relative accuracy of plausibility assessment based on the former and latter sources. The groups of scenarios can then be clustered based on this ratio; hence, such a ratio is an important parameter of human behavior associated with explaining something to other humans.
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