Inductive reasoning is the
development of a theory or a conclusion after consideration of several empirical
observations. Inductive reasoning means leading on to an action, or inducing.
Inductive reasoning is based on, or characterized by induction; using a method of
Deductive 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
The Development of
Inductive Reasoning: Cross-sectional Assessments in an Educational Context -
Beno Csapˇ, Attila Jˇzsef 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 (number analogies, verbal analogies, number series, verbal series,
coding, exclusion) were devised and administered to 3rd, 5th, 7th, 9th, and 11th grade
students (N 2000). Data were also collected on students school achievement, and a
test of applied science knowledge was administered to the two oldest samples. 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.
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. Ten normal volunteers performed deductive and
inductive reasoning tasks while their regional cerebral blood flow pattern was recorded
using [15O] H2O PET imaging. 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. The deduction condition resulted in activation of the left inferior frontal
gyrus (Brodmann areas 45, 47). The induction condition resulted in activation of a large
area comprised of the left medial frontal gyrus, the left cingulate gyrus, and the left
superior frontal gyrus (Brodmann areas 8, 9, 24, 32). Induction was distinguished from
deduction 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 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. As part of a series of
research on cognitive trait model that aim to allow virtual learning environments to
provide adaptive support for the learner's cognitive capacity, 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
Franšois E. Cellier, Institute of Computational Science, ETH ZŘrich
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. Recently, optimal recoding techniques have been devised which are presented in
this paper. The forecasting power of the Inductive Reasoning approach has been shown to be
dramatic in a number of examples. Yet, the forecast was always expressed in terms of the
recoded, i.e. the discrete, variables, and not in terms of the original continuous
variables. Recently, we have been working on a modification of the technique which allows
us to reconstruct the continuous signals from the forecast discrete signals with very good
accuracy. For this purpose, we exchanged the previously used probabilistic quality
measures for fuzzy quality measures, and we predict, together with the discrete states
also new fuzzy membership functions of the forecast signals.
Inductive Reasoning, Bounded Rationality and the Bar Problem - W.
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
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.
Across the first five experiments, however, participants generalized the disadvantage of
women at the aggregate level to judgments referring to the different levels of the
covariate, even when motivation was high and appropriate mental models were activated.
Time Series Prediction Using Inductive Reasoning Techniques
Josefina Lˇpez Herrera, Professora Associada, Llenguatges i Sistemes InformÓtics
Universitat PolitŔcnica 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 the identification of systems, very good results of
using this methodology had been reported earlier.
This thesis is structured into eight chapters and two appendices.
In Chapter 3, the state of the art of the Fuzzy Inductive Reasoning methodology is
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. Moreover, a single exponential model of response choice
provided a good fit to each data set. The single parameter estimate for this model was
roughly comparable across tasks. In Experiment 2, 36 subjects completed a timed
tachistoscopic test in which they, too, were asked to solve 90 induction items, equally
divided among the three kinds of induction items noted above. The subjects' task was to
choose the better of two response options as a completion for each particular item.
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
While conclusions can be wrong if observations are faulty or are drawn from an
unrepresentative sample, if properly used, inductive reasoning can be incredibly powerful.
Indeed, it lies at the root of the scientific method that has done so much to advance
humanity in the last 500 years. 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 (in a
practical sense), good managers can build on these conclusions and move forward
effectively and successfully. - mindtools.com
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, is known to converge for 2 Î2 games, 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.
Developing Measures of Inductive Reasoning Using Logic-Based Measurement
- Tutorial - ipmaac.org
How is Inductive Reasoning Different from Deductive 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.
The truth of the evidence makes the truth of the conclusion certain.
Arthur, W. B., 1994. Inductive reasoning and bounded rationality.
American Economic Review 84 (2), 406-11.
Deductive versus Inductive Reasoning.