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In recent years, archaeological research has relied on large datasets, both temporally and geographically, with some archaeologists discussing that there should be a shift towards a more scientific form of conducting archaeological research called “macroarchaeology.” Ironically, and contradictorily, this shift towards large-scale research has involved the use of inductive approaches, which means that archaeological material needs to be converted into universal quantitative values. The inductive approaches used by archaeologists today, as argued by Karl Popper, and other authors in recent years, cannot be considered scientific in the strict sense of the word, since there is always a degree of uncertainty in inductive reasoning. This study suggests that archaeological data can be considered as traces of the past, clues that allow us to reconstruct past phenomena. As Carlo Ginzburg’s evidential paradigm demonstrates, thinking of the past in terms of traces and clues is much more scientific than appears at first. In addition to traces and clues, a second interpretative procedure can be conducted on data. Based on Ginzburg’s conjectural paradigm and discussion on fiction, we can recognize the past as real, while at the same time, conjecture the several ways past agents could have acted otherwise.

In a discussion of Sherlock Holmes' "science of deduction" and the related "method of exclusion," I show that Holmes' claim that his inferences are deductive makes sense, if we consider his theoretical presuppositions. So, it is more accurate to say that he tries to reduce abduction to deduction than that he confuses them. His theoretical framework, albeit inadequate as a theory of empirical reasoning, can be seen as a basic model of classical (symbolic) AI. The main problems of this approach are surveyed, and abduction is brought into play as both a better characterization of Holmes' inferences and a better guide for building AI systems. This is a good background for raising the question of creativity because, according to Peirce, it relates to abduction in a substantial way. Is Sherlock Holmes creative? Can machines be creative? An affirmative answer to the second question might be given by a "logic of discovery" but the problem remains that inventing such a logic already presupposes creativity. [ABSTRACT FROM AUTHOR]

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In this article, we want to analyze some of the limits of artificial intelligence. In order to have a significant development of this type of technology, one of the problems that requires a fundamental approach is the problem of inference. We will explore three types of inferences: deduction, induction, and abduction. Deduction was the basis for developing classical Artificial Intelligence (expert or symbolic system). Induction is used by modern Artificial Intelligence (neural networks), which requires a massive amount of data and very high computing powers. General Artificial Intelligence (currently in research) needs a theory of abduction that experts do not yet know how to program into a computational system. [ABSTRACT FROM AUTHOR]

In the first three articles of this series, convincing arguments were put forward as to why contemporary natural scientific observations can best be explained if they are interpreted within a paradigm of an all-powerful Creator. In line with this observation, a growing number of eminent scholars fundamentally differ from the materialistic position that metaphysical theories deal with concepts that are not empirically observable and testable. Instead, these scholars postulate that metaphysical theories can indeed be used to make predictions about the natural world and that they are therefore falsifiable. A few examples include the fact that a theistic cosmology expects that the universe had a moment of beginning, which presupposes that a transcendent Creator must already have accurately established the physical conditions for all empirical observations, before the moment of creation. Empirical observations of the universe confi rm this theory, as described in the fi rst article of this series. More examples of the predictive value of a metaphysical theory, are the origin of information contained in DNA, as described in article 2, as well as in empirical data on the origin of humans (see article 3). The reference to metaphysical theories as pseudo-science has therefore been repudiated by recent empirical evidence and a growing number of authoritative researchers, do recognise the fact that a “God hypothesis” would appear to be scientifi cally accountable. Yet, there remains great opposition among mainstream natural scientists to any hint of metaphysical explanations for contemporary observations. Materialist scientists accuse religious scientists of approaching the Enlightenment and Modernism in reverse – of wanting to take science back to the dark ages, where the church dictated to science what may be believed. However, it is ironic that the “God of the gaps”- argument is replaced by normative materialism with a “science of the gaps”- argument, where it is argued that sooner or later science will provide humanity with a “theory of everything”. To decide in advance that a certain explanation for empirical evidence may not be considered, is unacceptable in natural science. Yet mainstream science takes exactly this position regarding cosmology and the origin of life. In this article, the issue of normative materialism is scrutinised. What is the deepest origin of the reluctance of many researchers, to put forward a metaphysical explanation for scientifically observed data? Even the mere consideration of such a hypothesis is rejected. Why don’t people even consider the possibility that a Creator played (and still plays) a role in the universe? The main conclusion, based on an exposé of the scientific method, various ways of scientific reasoning and biblical evidence regarding creation and the origin of sin, is that the answer lies in mankind’s sinful, self-willed nature. If a scientist, based on irrefutable evidence, admits that God exists and that His works are visible in creation, as well as that empirical observations can be reconciled with biblical descriptions of the cosmos, he/she must then also admit that the Bible represents the truth. The necessary consequence of such an admittance is that such a person must then live in submissive obedience to God. This giving up of the “own self” is, however, directly against the nature of sinful man – every person has the need to be master of his/her own salvation and to answer only to himself/herself; something that is embodied in the Postmodernist Zeitgeist in which we fi nd ourselves. To rule out (and even formally forbid) one of the possible explanations for empirical observations in advance, only because it differs from a researcher’s worldview, does not make that researcher a better scientist. On the contrary – normative materialism and the accompanying grasping at straws by formulating new, fantastic, and difficult-to-test theories as alternatives to the existence of God, simply represent poor, invalid scientific practice. Even though natural science overwhelmingly testifies to the presence and involvement of God in creation, there are still numerous questions that cannot be answered by natural scientists, or by theologians. Natural scientists should, however, consider all possible explanations for empirical data with an equal measure of respect and attention in order to practise science with integrity. [ABSTRACT FROM AUTHOR]

It has been said that there is no scholarly consensus as to why Aristotle's logics of proof and refutation would have borne the title Analytics. But if we consulted Tarski's (Introduction to logic and the methodology of deductive sciences, Oxford University Press, New York, 1941) graduate-level primer, we would have the perfect title for them: Introduction to logic and to the methodology of deductive sciences. There are two strings to Aristotle's bow. The methodological string is the founding work on the epistemology of science, and the logical string sets down conditions on the proofs that bring this knowledge about. The logic of proof presents a difficulty whose solution exceeds its theoretical reach. The logic of refutation takes the problem on board, and advances a solution whose execution is framed by fallacy-avoidance at the beginning and fallacy-adoption at the end. But with a difference: the avoidance-fallacies are of Aristotle's own conception, whereas the adoption-fallacies, so judged in the modern tradition, aren't fallacies at all for Aristotle. The avoidance-fallacies are begging the question and ignoratio elenchi, and the adoption-fallacies, fallacies in name only, are the ad hominem and ad ignorantiam, an inductive turning in the first instance, and an abductive finish in the second. [ABSTRACT FROM AUTHOR]

The article highlights, from the perspective of neuroscience, the peculiarities of the investigator's modes of judgment, highlighting the common and specific characteristics of deductive, inductive and abductive inferences. The transition from certainty to possibility, quantified by probability, brings back, like Data Science, into discussion Thomas Bayes' subjective statistics, whose utility is not only in the classification and clustering of data but also in the dynamic quantification of probability in relation to evidence which the investigator has at a certain moment. [ABSTRACT FROM AUTHOR]

The pervasive use of AI today caused an urgent need for human-compliant AI approaches and solutions that can explain their behavior and decisions in human-understandable terms, especially in critical domains, so as to enforce trustworthiness and support accountability. The symbolic/logic approach to AI supports this need because it aims at reproducing human reasoning mechanisms. While much research has been carried out on single inference strategies, an overall approach to combine them is still missing. This paper claims the need for a new overall approach that merges all the single strategies, named MultiStrategy Reasoning. Based on an analysis of research on automated inference in AI, it selects a suitable setting for this approach, reviews the most promising approaches proposed for single inference strategies, and proposes a possible combination of deduction, abduction, abstraction, induction, argumentation, uncertainty and analogy. It also introduces the GEAR (General Engine for Automated Reasoning) inference engine, that has been developed to implement this vision. [ABSTRACT FROM AUTHOR]

This manuscript contributes to research on how algebraic thinking about operations and properties can develop, and relevant forms of curricular activity. The key question asked is: how can students' early algebraic activity be fostered through focusing on relations involving operations and properties? We adopt Radford's three aspects of algebraic thinking (indeterminacy, denotation and analyticity), which focus on considering, symbolising and operating on unknown objects. We investigate the work of a class of 11–12 year old students in re-analysing data collected over 20 years ago as part of a project exploring algebraic activity. Our findings point to the proposal that a focus on conjecturing about relationships can be a powerful route into early algebra. On the basis of our analysis we propose an extension to Radford's third aspect of algebraic thinking (analyticity) to include structuring a mathematical space, e.g., through a set of conjectures. [ABSTRACT FROM AUTHOR]

We propose an abductive logic of scientific inference for quantitative research. The advent of computational sociology has exposed the limitations of a deductive logic of inquiry for quantitative researchers due to a lack of traditional sociological variables and an abundance of unfamiliar variables and data formats, complicating hypothesis testing. In response, some researchers have embraced inductive inference, but inductive analysis without theoretical guidance risks producing atheoretical findings. An abductive logic of inquiry rests on developing new theoretical insights based on surprising research results in light of existing theories. In computational sociology, such surprising findings can be cultivated by taking advantage of the analytical potential of scaled-up data and developing flexible analytical and visualization procedures. We illustrate these tactics with a surprising finding in a study of the labor supply decisions of New York City yellow cab drivers.

In this paper, we set out to investigate the following question: if science relies heavily on induction, does philosophy of science rely heavily on induction as well? Using data mining and text analysis methods, we study a large corpus of philosophical texts mined from the JSTOR database (n = 14,199) in order to answer this question empirically. If philosophy of science relies heavily on induction, just as science supposedly does, then we would expect to find significantly more inductive arguments than deductive arguments and abductive arguments in the published works of philosophers of science. Using indicator words to classify arguments by type (namely, deductive, inductive, and abductive arguments), we search through our corpus to find patterns of argumentation. Overall, the results of our study suggest that philosophers of science do rely on inductive inference. But induction may not be as foundational to philosophy of science as it is thought to be for science, given that philosophers of science make significantly more deductive arguments than inductive arguments. Interestingly, our results also suggest that philosophers of science do not rely on abductive arguments all that much, even though philosophers of science consider abduction to be a cornerstone of scientific methodology. [ABSTRACT FROM AUTHOR]

This paper concerns the internal structure of reasoning that, basically consisting in conjecturing and refuting, is too often identified with only deducing, abducing and refuting, that is, with just the deductive search of consequences, hypotheses and refutations. With such identification, it is forgotten that in addition to consequences and hypotheses, there is a third class of conjectures, speculations or proper guesses. Speculations are inferentially non-comparable, or orthogonal, with the premise, and generate creativity. It is presented a very simple formal view for the structure of Commonsense Reasoning, a mathematical model allowing to show the importance of speculations, and specially to start with its systematic computational search. [ABSTRACT FROM AUTHOR]

In this paper, we propose an argumentation formalism that allows for both deductive and abductive argumentation, where 'deduction' is used as an umbrella term for both defeasible and strict 'forward' inference. Our formalism is based on an extended version of our previously proposed information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. In the current version, we consider additional types of information such as abstractions which allow domain experts to be more expressive in stating their knowledge, where we identify and impose constraints on the types of inferences that may be performed with the different types of information. A new notion of attack is defined that captures a crucial aspect of abductive reasoning, namely that of competition between abductively inferred alternative explanations. Our argumentation formalism generates an abstract argumentation framework and thus allows arguments to be formally evaluated. We prove that instantiations of our argumentation formalism satisfy key rationality postulates. [ABSTRACT FROM AUTHOR]

The paper proposes a way to naturalise Charles S. Peirce’s conception of the scientific method, which he specified in terms of abduction, deduction and induction. The focus is on the central issue of the economy of research in abduction and self-correction by error reduction in induction. We show how Peirce’s logic of science receives support from modern breakthroughs in computational neuroscience, and more specifically from Karl Friston’s statements of active inference and the Free Energy Principle, namely the account of how organisms’ capacity to decrease the discrepancy between the expected value and actual outcomes entails the minimisation of errors in their hypotheses about the world. A scientific account of organisms’ capacity to choose policies and form expectations is aligned with Peirce’s theories of abduction and induction, and especially with the economy of research. The upshot is the recovery of Peirce’s theory of the logic of science in the context of active inquiry. [ABSTRACT FROM AUTHOR]

This article offers the application of constructivist grounded theory in inquiring dimensions of accountability in education. The classical version of grounded theory, which was aligned with the positivist epistemology, has been well discussed in qualitative research. However, the constructivist version of grounded theorizing concerning education has not been adequately articulated in the existing literature. In response to this methodological gap, this paper discusses the canons of constructivist grounded theory in reference to inquiring school actors’ accountability for service delivery in education. The paper draws empirical evidence from the author’s study conducted in the context of community schools in Nepal. The paper argues that theory construction in constructivist grounded theory design is accomplished through the interaction of both data-indicated and extant theoretical concepts by integrating inductive, abductive and deductive reasoning during various stages of the inquiry. The paper also argues that, in constructivist grounded theory, it is not the data saturation as such but the level of researcher’s satisfaction where the grounded theorizing terminates. The paper concludes that the constructivist epistemology of grounded theorizing is useful in addressing the localized understanding of accountability in the decentralized context of education governance in Nepal.

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This article presents two parts of metascience, namely conceptology and methodology. It is noted that any theory consists of concepts: postulates, principles, laws and variables. Intratheoretical methodology studies ways to manage the concepts above, which are deduction, adduction (experiment), induction and abduction. Together, these four methods form an intratheoretical transduction. Induction includes dispersion, regression and functional analyses. Abduction is considered as a change of partially obsolete principles by new ones. It criticizes the point of view according to which scientific discovery is an act of insight, intuition. Theories are managed by the intertheoretical transduction. Its methods are problematization, innovation and interpretation. Realization of several cycles of intertheoretical transduction leads to the formation of a interpretative number of theories, which the author calls ligatheory. Scientific knowledge consists of ligatheories and relations between them. In case of interligatheoretical transduction, which methods are also problematization, innovation and interpretation, acceptor ligatheory uses achievements of the donor conception. All findings are interpreted according to sociology materials.

In this shop floor report I explore the process of constructing and reconstructing a coding frame for qualitative content analysis. Drawing on my research experience with the archives of the communist secret police, I highlight the advantages of employing controlled abductive reasoning in the process of coding frame construction and reconstruction. As I argue in the report, using abductive reasoning may help researchers to explore their data more fully, thus also facilitating a richer understanding of historical reality in social research. Importantly, with abductive reasoning theoretical innovation is also possible, because of the process of "defamiliarization" with data, and experimentation with various epistemological lenses to develop a new understanding of the phenomena in question.

Evidential statistics is an important advance in model and theory testing, and scientific reasoning in general, combining and extending key insights from other philosophies of statistics. A key desiderata in evidential statistics is the rigorous and objective comparison of alternative models against data. Scientific theories help to define the range of models which are brought to bear in any such assessment, including both tried and trusted models and risky novel models; such theories emerge from a kind of evolutionary process of repeated model assessment, where model selection is akin to natural selection acting both on the standing crop of genetic variation, and on novel mutations. The careful use of evidential statistics could play an important and as yet to be fulfilled role in the future development of scientific theories. We illustrate these ideas using examples from ecology and evolutionary biology.

In this shop floor report I explore the process of constructing and reconstructing a coding frame for qualitative content analysis. Drawing on my research experience with the archives of the communist secret police, I highlight the advantages of employing controlled abductive reasoning in the process of coding frame construction and reconstruction. As I argue in the report, using abductive reasoning may help researchers to explore their data more fully, thus also facilitating a richer understanding of historical reality in social research. Importantly, with abductive reasoning theoretical innovation is also possible, because of the process of "defamiliarization" with data, and experimentation with various epistemological lenses to develop a new understanding of the phenomena in question. [ABSTRACT FROM AUTHOR]

We claim that abduction should primarily be studied from the perspective of its use. The big question "What is abduction?" is most often interpreted substantively and this distracts attention from the instrumental aspect of this form of reasoning. We propose to address the problem by asking "How abduction is used?". As a result of our approach we see the fact that abduction needs to be construed as concerned with both generation and evaluation of hypotheses, and, furthermore, that abduction is a compound form of reasoning. [ABSTRACT FROM AUTHOR]

When do children acquire the ability to understand recursion—that is, repeated loops of actions, as in cookery recipes or computer programs? Hitherto, studies have focused either on unconscious recursions in language and vision or on the difficulty of conscious recursions—even for adults—when learning to program. In contrast, we examined 10- to 11-year-old fifth-graders' ability to deduce the consequences of loops of actions in informal algorithms and to create such algorithms for themselves. In our experiments, the children tackled problems requiring the rearrangement of cars on a toy railway with a single track and a siding—an environment that in principle allows for the execution of any algorithm—that is, it has the power of a universal Turing machine. The children were not allowed to move the cars, so each problem's solution called for them to envision the movements of cars on the track. We describe a theory of recursive thinking, which is based on kinematic simulations and which we have implemented in a computer program embodying mental models of the cars and track. Experiment 1 tested children's ability to deduce rearrangements of the cars in a train from descriptions of algorithms containing a single loop of actions. Experiment 2 assessed children's spontaneous creation of similar sorts of algorithms. The results showed that fifth-grade children with no training in computer programming have systematic abilities to deduce from and to create informal recursive algorithms. [ABSTRACT FROM AUTHOR]

Deduction, induction, and semeiotic relate by each being a permutation of modus ponens. As deduction providing necessary inference is the means of mathematical proof, and induction establishing sound generalizations is the basis of scientific research, so semeiotic bringing to bear informed responses to experience is the core of human understanding. Semeiotic, specifically semeiosis, is shown to be Peircean abduction in the context of signs, thus producing informed response to experience. [ABSTRACT FROM AUTHOR]

This article is devoted to the consideration of the question of constructing human reasoning. The author shows that the use of purely logical methods: deduction, induction, abduction - for making certain decisions or constructing certain inferences in real life is impossible. For a long time, anti-psychological position prevailed in logic, but it has become clear today that such artificial models of normative rationality, proposed by formal logic, are inadequate in their reflection of reality. Thus, it is time for logic to move away from ideal models to studying the empirical world. By the example of the famous imaginary British detective Sherlock Holmes, the author explains the action of the First and Second systems of thought (by Kahneman) and reveals the secret of Holmes' success in always «finding» the right conclusions. [ABSTRACT FROM AUTHOR]

The paper proposes a solution to the problem of counterfactuals building on both Rescher's epistemic and Tichý's semantic approaches. The core of the latter is the thesis that a speaker when expressing a true counterfactual assumes a set of background indicative premises as an implicit parameter. When added tacitly to an unreal antecedent, these premises entail the consequent logically or analytically. We argue against Pollock's impossibility objection concerning revision of the producer's beliefs. In accordance with the linguistic solution we distinguish between a relative and an absolute unreality of a course of events described in the consequent. Likewise, we draw a distinction between a potentially unreal and an absolutely unreal condition expressed in the antecedent. Drawing on our previous paper, Deductive and abductive retrodictions and predictions, we take counterfactuals with a positive time vector and an absolute unreal condition to be elided deductive judgements, i.e. deductive retrodictions, without any appeal to a special non-classical logic. Similarly, we consider counterfactuals with a positive time vector and a potentially unreal condition to be elided deductive predictions. [ABSTRACT FROM AUTHOR]

In the article I deal with some paradoxes and errors caused by improper usage of logical and philosophical terms appearing in the arguments for existence of God and other philosophical issues. I point at first some paradoxes coming from improper usage of propositional calculus as an instrument for analysis of a natural language. This language is actually not using simple sentences but rather propositional functions, their logical connections, and some replacements for variables in them. We still have to deal with so called paradox of material implication. The second paragraph provides formal and metatheoretical critics of Charles Sanders Peirce’s theory of deduction, induction and abduction. I argue that what Peirce and his followers call abduction is actually deduction or some reasoning unable to describe in terms of the logic used by them. Both syllogistic and inferential theory of abduction generate some paradoxes and contradictions. In the last paragraph also some paradoxes and contradictions resulting from the theory of causation by Jan Łukasiewicz are presented. The central issue of the article is erroneous usage of the implication: in logical paraphrases of a natural language, in description of the scientific reasoning, and in description of causality. However, my objective is not to solve all problems mentioned above but rather to open a discussion over them.