An explanation is a set of statements constructed to describe a set of facts which clarifies the causes, context, and consequences of those facts. This description may establish rules or laws, and may clarify the existing ones in relation to any objects, or phenomena examined. The components of an explanation can be implicit, and be interwoven with one another.
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.
- People often are not themselves clear on whether they are arguing for or explaining something.
- The same types of words and phrases are used in presenting explanations and arguments.
- The terms 'explain' or 'explanation,' et cetera are frequently used in arguments.
- Explanations are often used within arguments and presented so as to serve as arguments.
- A set of reasons
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 it.
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 of 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.
- Babbie, Earl (2007) The Practice of Social Research. (11th edition) Belmont, CA: Thompson Wadsworth.
- Remler, D.K. and Van Ryzin, G (2011). Research Methods in Practice. Thousand Oaks, CA: Sage Publications.
- Shields, Patricia and Rangarjan, N. 2013. A Playbook for Research Methods: Integrating Conceptual Frameworks and Project Management. . Stillwater, OK: New Forums Press. See Chapter three for an extended discussion of the connection between explanation as purpose and hypotheses as framework in empirical research. .
- Patricia M. Shields, Hassan Tajalli (2006). "Intermediate Theory: The Missing Link in Successful Student Scholarship". Journal of Public Affairs Education 12 (3): 313–334.
- Galitsky, Boris, de la Rosa, Josep-Lluis and Kovalerchuk, Boris Assessing plausibility of explanation and meta-explanation in inter-human conflict Engineering Application of AI V 24 Issue 8, pp 1472-1486, (2011).
- Galitsky, B., Kuznetsov SO Learning communicative actions of conflicting human agents J. Exp. Theor. Artif. Intell. 20(4): 277-317 (2008).
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