Semiparametric Block Bootstrap Prediction Intervals for Parsimonious Autoregression

This paper investigates the research question of whether the principle of parsimony carries over into interval forecasting, and proposes new semiparametric prediction intervals that apply the block bootstrap to the first-order autoregression. The AR(1) model is parsimonious in which the error term may be serially correlated. Then, the block bootstrap is utilized to resample blocks of consecutive observations to account for the serial correlation. The Monte Carlo simulations illustrate that, in general, the proposed prediction intervals outperform the traditional bootstrap intervals based on nonparsimonious models.

Hypergraph reconstruction from network data

Networks can describe the structure of a wide variety of complex systems by specifying which pairs of entities in the system are connected. While such pairwise representations are flexible, they are not necessarily appropriate when the fundamental interactions involve more than two entities at the same time. Pairwise representations nonetheless remain ubiquitous, because higher-order interactions are often not recorded explicitly in network data. Here, we introduce a Bayesian approach to reconstruct latent higher-order interactions from ordinary pairwise network data. Our method is based on the principle of parsimony and only includes higher-order structures when there is sufficient statistical evidence for them. We demonstrate its applicability to a wide range of datasets, both synthetic and empirical.

Postscript

This postscript reflects on the role of parsimony in the future of systematics. Under the view of systematics advocated in this book, the exuberantly messy data of biological diversity are organized into a clear and coherent explanatory framework through the application of the principle of parsimony. The principle of common cause, the principle of cause and effect, and the principle of uniformitarianism are all applications of the principle of parsimony to the explanation of events unfolding in time. Thus, parsimony is not merely an old-fashioned phylogenetic method that has been superceded by purportedly more powerful and sophisticated statistical tools: it is the epistemological key to evaluating empirical evidence and discovering orderly patterns in the world to the extent that our perceptions allow. Ultimately, the success of every scientific inference and prediction relating to empirical phenomena in the world hinges upon parsimony.

Correlated Evolution in the Small Parsimony Framework

When studying the evolutionary relationship between a set of species, the principle of parsimony states that a relationship involving the fewest number of evolutionary events is likely the correct one. Due to its simplicity, this principle was formalized in the context of computational evolutionary biology decades ago by, e.g., Fitch and Sankoff. Because the parsimony framework does not require a model of evolution, unlike maximum likelihood or Bayesian approaches, it is often a good starting point when no reasonable estimate of such a model is available.In this work, we devise a method for detecting correlated evolution among pairs of discrete characters, given a set of species on these characters, and an evolutionary tree. The first step of this method is to use Sankoff’s algorithm to compute all most parsimonious assignments of ancestral states (of each character) to the internal nodes of the phylogeny. Correlation between a pair of evolutionary events (e.g., absent to present) for a pair of characters is then determined by their (co-) occurrence patterns among their respective ancestral assignments. We implement this method: parcours (PARsimonious CO-occURrenceS) and use it to study the correlated evolution among vocalizations in the Felidae family, revealing some interesting results.The parcours tool is freely available at https://github.com/murraypatterson/parcours

Is It Worthy to Take Account of the “Guessing” in the Performance of the Raven Test? Calling for the Principle of Parsimony for Test Validation

The present study compares the fit of two- and three-parameter logistic (2PL and 3PL) models of item response theory in the performance of preschool children on the Raven’s Colored Progressive Matrices. The test of Raven is widely used for evaluating nonverbal intelligence of factor g. Studies comparing models with real data are scarce on the literature and this is the first to compare models of two and three parameters for the test of Raven, evaluating the informational gain of considering guessing probability. Participants were 582 Brazilian’s preschool children ( Mage = 57 months; SD = 7 months; 46% female) who responded individually to the instrument. The model fit indices suggested that the 2PL fit better to the data. The difficulty and ability parameters were similar between the models, with almost perfect correlations. Differences were observed in terms of discrimination and test information. The principle of parsimony must be called for comparing models.

Efficient Inference of Optimal Decision Trees

Inferring a decision tree from a given dataset is a classic problem in machine learning. This problem consists of building, from a labelled dataset, a tree where each node corresponds to a class and a path between the tree root and a leaf corresponds to a conjunction of features to be satisfied in this class. Following the principle of parsimony, we want to infer a minimal tree consistent with the dataset. Unfortunately, inferring an optimal decision tree is NP-complete for several definitions of optimality. For this reason, the majority of existing approaches rely on heuristics, and the few existing exact approaches do not work on large datasets. In this paper, we propose a novel approach for inferring an optimal decision tree with a minimum depth based on the incremental generation of Boolean formulas. The experimental results indicate that it scales sufficiently well and the time it takes to run grows slowly with the size of datasets.

God as an All-Knowing Distributor of Happiness

This chapter investigates Lawrence Pasternack’s interpretation of God’s role in Kant’s philosophy, in relation to the concepts of morality, divine action, and grace. Pasternack praises what he finds to be a consistent strand of Kant’s soteriology, where God acts as a cognizer of our moral status, whereby God distributes happiness proportionately, when integrating and coordinating a moral world. It is conceded that such a role has two satisfactory features: it is something that God can do, consistent with our freedom, and it is something only God can do, it would seem, given God’s omniscience. Pasternack claims that even if Kant’s account departs from traditional Christianity, it nonetheless ‘offers us a coherent, consistent, unified, and intellectually mature way of thinking about sin, faith, salvation, and worship’. I argue that even if the coherence and significance of Kant’s soteriology is granted, we are still not yet presented with persuasive grounds for being required to believe in God, given all that is achieved by Kant’s noumenal intelligible realm, and given Kant’s principle of parsimony, which involves not believing in more than we need to, for the purposes of practical reason. For all we know, it still seems perfectly ‘thinkable’ that the noumenal moral realm of reasons, taken in itself and ‘without God’, is such that the highest good is possible. It is argued that this possibility could remain thinkable for Kant, even when reflecting upon natural evil.

For a Society Beyond Justice

The first part of the paper suggests a minimalist framework for integrating the discipline of psychology. Such a framework must rest on the principle of parsimony, build on fewest and simplest possible axiomatic assumptions and yet be able to contain, sustain and benefit from the enormous diversity of its interests and approaches, its ever-widening research enterprise and its unique relationship to neuroscience that situates it felicitously between natural and human sciences. I take the Kantian questions about the human condition as the starting point to link the concepts of reason, work and autonomy to the idea of self as transformative agency that straddles the domains of nature and society the way psychology does. The second part of the paper takes Rawls’ idea of society beyond justice, with reference Marx, to show why justice must act as the placeholder for equality in formal procedural democracies and to chart the direction that psychology should follow to meet the challenge of becoming more relevant for human welfare in this neoliberal age of deepening inequalities and mounting social, political and personality crises.

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

Avoiding “False Alarms” and “Misses” in Psychological Theory Building: Complementing the Principle of Parsimony With the Principle of Tentative Affirmation

Scientific parsimony is a reliable safeguard against speculative or ideological theorizing. But it can also hamper the advent of novel ideas and advanced paradigms if misapplied within the context of a conservative thinking style. We illustrate how the principle of parsimony is ideally suited to reduce a particular type of errors—namely the premature acceptance of speculative theories. In a signal-detection framework, such errors are called “false alarms”; but signal detection theory also points to another category of errors—namely “misses,” that is, the failure to acknowledge a positively existing but elusive phenomenon. The methodological repertoire of our falsification-oriented science does not provide a similarly rigorous tool to avoid this caliber of errors. In this paper, we hence argue for introducing a complementary principle—that of tentative affirmation—to also reduce the risk of “misses.” We illustrate this latter principle using the example of conceptual, nonbiological facets of psychological phenomena. We propose a roadmap that consults both principles—parsimony and tentative affirmation—in tandem to help researchers shield their theories against one-sidedness.