Inductive reasoning is the development of a theory or a conclusion after consideration of several observations with empirical evidence. Inductive reasoning means leading on to an action, or inducing. Inductive reasoning is based on, or characterized by using a method of induction. Grounded Theory is derived through inductive reasoning.
How is Inductive Reasoning Different from Deductive Reasoning?
In Inductive reasoning, the evidence does not guarantee the truth of the conclusion, but it gives us a good reason to believe in the truth of the conclusion. The premises support the conclusion.
In Deductive reasoning, the truth of the evidence makes the truth of the conclusion certain.
The Development of Inductive Reasoning: Cross-sectional Assessments in an Educational Context - Beno Csapo, Attila Jozsef University, Szeged, Hungary. This paper links two research paradigms, one that studies attributes and mechanisms of inductive reasoning and one that tries to make school learning more meaningful and knowledge better understood and more easily applied, by examining how inductive reasoning develops during a significant age range of schooling and how it relates to certain other cognitive functions. Six tests of inductive reasoning were devised and administered to 3rd, 5th, 7th, 9th, and 11th grade students (N 2000). The comparison of age groups indicated that the fastest development of inductive reasoning took place between the 5th and 9th grades; a major development was detected before the 5th grade, and only modest changes were found after the 9th grade. Regression analysis models indicated that inductive reasoning accounted for around twice as large a proportion of the results of the test that measured the applied science knowledge in everyday situations as did school knowledge.
Deductive Reasoning versus Inductive Reasoning - Noah D. Alper - In his History of Civilization in England, Henry Thomas Buckle makes some interesting observations on the respective merits of the deductive and inductive methods of propagating thought in the development of civilization. In the deductive method we begin with a general conclusion and then attempt to point out the facts which support it. In the inductive method we first select our facts and then seek to lead to the acceptance of the general conclusions or principles.
The seats of reason? An
imaging study of deductive and inductive reasoning. Goel V, Gold B, Kapur S,
Houle S. - Department of Psychology, York University, North York, Ontario, Canada.
We carried out a neuroimaging study to test the neurophysiological predictions made by different cognitive models of reasoning. In the control condition subjects semantically comprehended sets of three sentences. In the deductive reasoning condition subjects determined whether the third sentence was entailed by the first two sentences. In the inductive reasoning condition subjects reported whether the third sentence was plausible given the first two sentences. Inductive reasoning was distinguished from deductive reasoning by the involvement of the medial aspect of the left superior frontal gyrus (Brodmann areas 8, 9). These results are consistent with cognitive models of reasoning that postulate different mechanisms for inductive reasoning and deductive reasoning and view deduction as a formal rule-based process.
Supporting Inductive Reasoning in Adaptive Virtual Learning Environment
T. Lin, Kinshuk, and P. McNab (New Zealand)
Abstract: Inductive reasoning ability is one of most important mental abilities that give rise to human intelligence and is regarded as the best predictor for academic performance. However, most of the adaptive virtual learning environments tailor the learning material adaptively according to only learners' domain performance thus leaving learner's cognitive capacity, such as inductive reasoning ability, unsupported. This paper presents the finding on the particular issue of how individual's inductive reasoning capability can be supported using adaptive techniques for improved learning performance.
Fuzzy Measures in Inductive Reasoning
Donghui Li, Physical Design (CAD), LSI Logic Corporation
Francois E. Cellier, Institute of Computational Science, ETH Zurich
Abstract: Inductive Reasoning is a technique which allows us to reason about a finite state representation of a system on the basis of available data. If the data stem from a continuous system, they are first discretized (recoded) into a finite set of discrete values. The forecasting power of the Inductive Reasoning approach has been shown to be dramatic in a number of examples.
Inductive Reasoning, Bounded Rationality and the Bar Problem -
W. Brian Arthur.
Abstract: This paper draws on modern psychology to argue that as humans, in economic
decision contexts that are complicated or ill-defined, we use not deductive, but inductive
reasoning. That is, in such contexts we induce a variety of working hypotheses or mental
models, act upon the most credible, and replace hypotheses with new ones if they cease to
work. Inductive reasoning leads to a rich psychological world in which an agents
hypotheses or mental models compete for survival against each other, in an environment
formed by other agents hypotheses or mental models, a world that is both
evolutionary and complex. Inductive reasoning can be modeled in a variety of ways.
Inductive Reasoning and Judgment Interference: Experiments on Simpsons Paradox. Klaus Fiedler, Eva Walther, Peter Freytag, Stefanie Nickel, University of Heidelberg. In a series of experiments on inductive reasoning, participants assessed the relationship between gender, success, and a covariate in a situation akin to Simpsons paradox: Although women were less successful then men according to overall statistics, they actually fared better then men at either of two universities. Understanding trivariate relationships of this kind requires cognitive routines similar to analysis of covariance.
Time Series Prediction Using Inductive Reasoning Techniques
Josefina Lopez Herrera, Professora Associada, Llenguatges i Sistemes Informotics
Universitat Politecnica de Catalunya
Abstract: In this dissertation, new elements are described that have been added to the methodology of Fuzzy Inductive Reasoning (FIR), elements that allow the prediction of the future behavior of time series. In Chapter 3, the state of the art of the Fuzzy Inductive Reasoning methodology is presented.
Unities in Inductive Reasoning
Corporate Author : YALE UNIV NEW HAVEN CT DEPT OF PSYCHOLOGY
Sternberg,Robert J. ; Gardner,Michael K.
Abstract : Two experiments sought to discover sources of communalities in performance on three inductive reasoning tasks: analogies, series completions, and classifications. In Experiment 1, 30 subjects completed an untimed pencil-and-paper test in which they were asked to solve 90 induction items, equally divided among the three kinds of induction items noted above. The subjects' task was to rank-order four response options in terms of their goodness of fit as completions for each particular item. Data sets for the three tasks were highly intercorrelated, suggesting the possibility of a common model of response choice across tasks.
Inductive reasoning involves making useful generalizations about the environment as a whole, based on a necessarily limited number of observations. As such, it is an important tool that people use to build the models of reality they need to function effectively. If properly used, inductive reasoning can be incredibly powerful. Properly-applied scientific method is inductive reasoning in its purest form. At the core of inductive reasoning is the ability to look at outcomes, events, ideas and observations, and draw these together to reach a unified conclusion. Considering this, an experienced business person can use his or her own experiences to draw conclusions about current situations and solve problems based on what he or she has known to work in the past in similar situations. By accepting conclusions derived from inductive reasoning as “true” managers can build on these conclusions and move forward effectively and successfully.
The geometry of inductive reasoning in games
Abstract: Summary. This paper contributes to the recent focus on dynamics in noncooperative games when players use inductive learning. The most well-known inductive learning rule, Brown's fictitious play, yet many examples exist where fictitious play reasoning fails to converge to a Nash equilibrium. Building on ideas from chaotic dynamics, this paper develops a geometric conceptualization of instability in games, allowing for a reinterpretation of existing results and suggesting avenues for new results.