Theoretic Framework Research Articles

Bayesian Inferences on Uncertain Ranks and Orderings: Application to Ranking Players and Lineups

It is common to be interested in rankings or order relationships among entities. In complex settings where one does not directly measure a univariate statistic upon which to base ranks, such inferences typically rely on statistical models having entity-specific parameters. These can be treated as random effects in hierarchical models characterizing variation among the entities. In this paper, we are particularly interested in the problem of ranking basketball players in terms of their contribution to team performance. Using data from the National Basketball Association (NBA) in the United States, we find that many players have similar latent ability levels, making any single estimated ranking highly misleading. The current literature fails to provide summaries of order relationships that adequately account for uncertainty. Motivated by this, we propose a Bayesian strategy for characterizing uncertainty in inferences on order relationships among players and lineups. Our approach adapts to scenarios in which uncertainty in ordering is high by producing more conservative results that improve interpretability. This is achieved through a reward function within a decision theoretic framework. We apply our approach to data from the 2009–2010 NBA season.

Characterizing the circadian connectome of Ocimum tenuiflorum using an integrated network theoretic framework

Across the three domains of life, circadian clock is known to regulate vital physiological processes, like, growth, development, defence etc. by anticipating environmental cues. In this work, we report an integrated network theoretic methodology comprising of random walk with restart and graphlet degree vectors to characterize genome wide core circadian clock and clock associated raw candidate proteins in a plant for which protein interaction information is available. As a case study, we have implemented this framework in Ocimum tenuiflorum (Tulsi); one of the most valuable medicinal plants that has been utilized since ancient times in the management of a large number of diseases. For that, 24 core clock (CC) proteins were mined in 56 template plant genomes to build their hidden Markov models (HMMs). These HMMs were then used to identify 24 core clock proteins in O. tenuiflorum. The local topology of the interologous Tulsi protein interaction network was explored to predict the CC associated raw candidate proteins. Statistical and biological significance of the raw candidates was determined using permutation and enrichment tests. A total of 66 putative CC associated proteins were identified and their functional annotation was performed.

Challenges of Translating English Pragmatic Markers into Kurdish by EFL Students

This study aimed to investigate the translation of Pragmatic Markers (PMs) by Kurdish EFL students. This study demonstrates an innovative method/practice of utilizing translation to study the linguistic phenomena, Pragmatic Markers (PMs) (cf. Fischer 2006; Fraser, 1999; Aijmer, 2002). Based on the Relevance Theoretic Framework and polysemy approach, this paper consolidates research that examines the challenges that face EFL students of English Department at Salahaddin University, Erbil. The data were analyzed quantitatively and qualitatively. The results revealed that Kurdish EFL students use a variety of PMs in their writing, with some PMs being overused and others being underused. The study suggests that teachers should help Kurdish EFL students to use PMs effectively in their writing. Also, the results showed that the majority of participants had difficulty in recognizing the more complex adversative and causal PMs, while they had very few issues with Additive PMs. In conclusion, Kurdish EFL students may recognize Additive PMs and translate them with less difficulty than identifying more complex adversative and causal PMs.

An information theoretic Bayesian uncertainty analysis of AEM systems over Menindee Lake, Australia

SUMMARY Long-range, active-source airborne electromagnetic (AEM) systems for near-surface conductivity imaging fall into two categories: helicopter (rotary-wing) borne or fixed-wing aircraft borne. A multitude of factors such as flying height, transmitter loop area and current, source waveforms, aerodynamic stability and data stacking times contribute to the geological resolvability of the subsurface. A comprehensive comparison of the relative merits of each system considering all such factors is difficult, but test flights over well-constrained subsurface geology with downhole induction logs are extremely useful for resolution studies. However, given the non-linear nature of the electromagnetic inverse problem, handling transmitter–receiver geometries in fixed-wing aircraft is especially challenging. As a consequence of this non-linearity, inspecting the closeness of downhole conductivities to deterministic inversion results is not sufficient for studying resolvability. A more comprehensive picture is provided by examining the variation in probability mass of the depth-wise Bayesian posterior conductivity distributions for each kind of AEM system within an information theoretic framework. For this purpose, probabilistic inversions of data must be carried out. Each acquiring system should fly over the same geology, survey noise levels must be measured and the same prior probabilities on conductivity must be used. With both synthetic models as well as real data from over the Menindee calibration range in New South Wales, Australia, we shed new light on the matter of AEM inverse model uncertainty. We do this using two information theoretic attributes derived from different Kullback–Leibler divergences—Bayesian information gain, and a strictly proper scoring rule, to assess posterior probabilities estimated by a novel Bayesian inversion scheme. The inversion marginalizes fixed-wing geometry attributes as generic nuisance parameters during Markov chain sampling. This is the first time-domain AEM study we know of, that compares nuisance marginalized subsurface posterior conductivities from a fixed-wing system, with rotary-wing derived posterior conductivities. We also compare field results with induction log data where available. Finally, we estimate the information gain in each case via a covariate shift adaptation technique that has not been used before in geophysical work. Our findings have useful implications in AEM system selection, as well as in the design of better deterministic AEM inversion algorithms.

Heisenberg models and Schur–Weyl duality

We present a detailed analysis of certain quantum spin systems with inhomogeneous (non-random) mean-field interactions. Examples include, but are not limited to, the interchange- and spin singlet projection interactions on complete bipartite graphs. Using two instances of the representation theoretic framework of Schur–Weyl duality, we can explicitly compute the free energy and other thermodynamic limits in the models we consider. This allows us to describe the phase transition, the ground-state phase diagram, and the expected structure of extremal states.

Exploring Fairer Post-Growth Grounding by Coupling Social Economy with Cultural and Creative Industry

This article aims to explore and provide a possible theoretic framework and practical direction, toward the needed transformation in the sphere of cultural development. The central hypothesis is that a social and solidarity economy, i.e., social entrepreneurship is a more suitable framework, than a traditional neoclassical (profitable-oriented economy) for the future desired development of the cultural and cre-ative sector. By understanding the common grounding, “beyond the profit orientation”, social economy offers a more suitable path to deal with the specific market spheres of culture. Crossings of theoretical and empirical findings enabled us to bring the conclusions of the existing practices, and actors, through a deeper understanding of CCI development. According to the existing practices, we can conclude that practical creative entrepreneurship in the Belgrade design district is socially responsible and sustainable, started and led by creativity, innovation, and experimental work, while simultaneously contributing to socially important issues.

A game theory‐based approach for the study of inter‐interference in coexisting wireless body area networks

SummaryA wireless body area network (WBAN) enables the continuous monitoring of health conditions including heart rate, temperature, and glucose levels. It is composed of several sensors that are placed on the body. The network's performance is significantly affected by the unpredictable movements of the human body. Due to the varying proximities between them, the existence of several WBANs creates a challenge when mobility is involved. In this fictitious scenario, we consider a park area with both mobile and static WBANs or WBAN‐equipped individuals passing through the park who have varying degrees of mobility. When a mobile WBAN passes a static WBAN and is within a minimum distance of the static WBAN, they pair up and immediately interfere. Inter‐interference caused by WBANs operating in a limited area causes packet loss and performance deterioration. In this work, static WBAN locations are optimised to minimise interference and system inter‐interference problems are addressed by a game theoretic method. This paper formulates a flexible game theoretic framework to study WBAN coexistence using the expected pay‐off function considering a two‐player game. It is observed from the results obtained that the probability of interference caused by fast mobile WBANs is reduced to a maximum of 50% compared with that caused by slow mobile WBANs thereby reducing the need for relocation of static WBANs.

Planning for Attacker Entrapment in Adversarial Settings

In this paper, we propose a planning framework to generate a defense strategy against an attacker who is working in an environment where the defender can operate covertly. Here the objective of the defender is to covertly guide the attacker to a trap state from which the attacker cannot achieve their goal. Further, the defender is constrained to achieve its goal within a K number of steps, where K is calculated as a pessimistic lower bound within which the attacker is likely to not suspect any foul play in the environment. Such a defense strategy is highly useful in real-world systems like honeypots or honeynets where an unsuspecting attacker interacts with a simulated production system while assuming it is the actual production system. Typically, the game between an attacker and a defender is captured using game theoretic frameworks. However, our problem formulation allows us to capture it as a much simpler infinite-horizon discounted MDP where the optimal policy to the MDP gives the defender's strategy against the actions of the attacker. Through empirical evaluation, we show the merits of our problem formulation.

Will democracy support switching from targeting to universalism?

This paper takes advantage of Ide, Furuichi, and Miyazaki’s policy proposal to present a theoretic framework for analyzing the likelihood that a democratic society will support moving social policies from targeting to universal access. The main finding is that the proposed transition to universalism will likely increase Japan’s already substantial fiscal deficit and that it will appease social cleavages only if the psychological feeling of equal treatment is large enough to compensate the poor citizen’s utility loss. However, if the equal-treatment feeling is not high enough, then both the poor and the middle class will be dissatisfied with the transition proposal, which will not gather social support.

Enabling High-Quality Machine Learning Model Trading on Blockchain-Based Marketplace

Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage its advantages on a broader scale. However, these markets face challenges in designing effective incentives for model owners to share their models, and for model users to provide honest feedback on model quality. This paper proposes a novel game theoretic framework for machine learning model sharing markets that addresses these challenges. Our framework includes two main components: a mechanism for incentivizing model owners to share their models, and a mechanism for encouraging the honest evaluation of model quality by the model users. To evaluate the effectiveness of our framework, we conducted experiments and the results demonstrate that our mechanism for incentivizing model owners is effective at encouraging high-quality model sharing, and our reputation system encourages the honest evaluation of model quality.

QTPI: A quasi-toxicity probability interval design for phase I trials with multiple-grade toxicities.

The common terminology criteria for adverse events by the National Cancer Institute has greatly facilitated the revolution of drug development and an increasing number of Phase I trials have started to collect multiple-grade toxicity endpoints. Appropriate and yet transparent Phase I statistical designs for multiple-grade toxicities are therefore in great needs. In this article, we propose a quasi-toxicity probability interval (qTPI) design that incorporates a quasi-continuous measure of the toxicity probability () into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to according to a severity weight matrix. Dose-toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. Finally, a hypothetical soft tissue sarcoma trial with six toxicity types and grade 0 to grade 4 severity grades is illustrated with patient-by-patient dose allocation under the qTPI design.

Feasibility of Backward Compatible MIMO Broadcasting: Issues in SISO-MIMO Coexistence

This paper introduces the application of backward compatible (B-Comp) MIMO for simultaneously supporting inter-tower communications network (ITCN) and legacy digital television (DTV) service. In B-Comp MIMO systems, SISO and MIMO broadcasting are overlaid on the same frequency, where the SISO transmission is subject to compliance with legacy system protocol. In this work, the several configurations of B-Comp MIMO are proposed in terms of physical layer multiplexing (e.g., layered or time-division multiplexing) and antenna assignment in legacy transmission, and accordingly, system-specific issues on each configuration affecting the performance degradation or practical realization are discovered. Moreover, the spectral efficiency performance of each configuration is derived and analyzed under the system-specific issues in terms of the achievable throughput for ITCN while guaranteeing the legacy service’s requirements. Through such investigations with qualitative inspections, theoretic analysis, and numerical simulations, practical observations are modeled in a theoretic framework, and performance-objected tradeoffs are explicitly revealed to allow insightful decisions to answer which configuration is optimal for each use case. Lastly, the performance penalties from pilot-related limitations are delved into and hence a comprehensive inspection is allowed in a practical view.

A game theoretic framework for post-consumer recycled and new packaging industries

This paper presents a game-theoretic framework of competition for two packaging companies. The first one is a traditional packaging firm that employs new raw materials in its packaging and invests in innovative packaging solutions. In contrast, the other takes advantage of a combination of new and post-consumer-recycled (PCR) material based on a cost saving strategy. The model incorporates the price and quality of the product as well as its packaging and is implemented on packaging firms with the same source of manufacturer/importer. Two different scenarios are considered with the potential to push companies towards sustainable solutions; namely the existence of eco-conscious consumers and government tax on the new raw material packaging. The problem is established as a two-stage game, and the optimal level of investment in packaging innovation, the optimal ratio of PCR material and the optimal retail prices are derived, all in equilibrium. A real-world example and also parametric and sensitivity analysis are provided and valuable insights are derived. The results indicate that current proposed virgin tax plans do not provide a strong incentive towards sustainable packaging, whereas eco-conscious customers give this impetus. It was also observed that the imposed virgin tax does not impede innovation investment in virgin packaging companies.

The Pragmatics of Fiction: Literature, Stage and Screen Discourse

The Pragmatics of Fiction: Literature, Stage and Screen Discourse

A game theoretic approach to balance privacy risks and familial benefits

As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users’ valuations of the service will affect a population’s behavior. In this paper, we present a game theoretic framework to model interdependent privacy challenges in genomic data sharing online. Through simulations, we find that in addition to the boundary cases when (1) no player and (2) every player joins, there exist pure-strategy Nash equilibria when a relatively small portion of players choose to join the genomic database. The result is consistent under different parametric settings. We further examine the stability of Nash equilibria and illustrate that the only equilibrium that is resistant to a random dropping of players is when all players join the genomic database. Finally, we show that when players consider the impact that their data sharing may have on their relatives, the only pure strategy Nash equilibria are when either no player or every player shares their genomic data.

Integrating eco‐evolutionary dynamics into matrix population models for structured populations: Discrete and continuous frameworks

AbstractState‐structured populations are ubiquitous in biology, from the age‐structure of animal societies to the life cycles of parasitic species. Understanding how this structure contributes to eco‐evolutionary dynamics is critical not only for fundamental understanding but also for conservation and treatment purposes. Although some methods have been developed in the literature for modelling eco‐evolutionary dynamics in structured population, such methods are wholly lacking in the function evolutionary game theoretic framework.In this paper, we integrate standard matrix population modelling into the function framework to create a theoretical framework to probe eco‐evolutionary dynamics in structured populations. This framework encompasses age‐ and stage‐structured matrix models with basic density‐ and frequency‐dependent transition rates and probabilities.For both discrete and continuous time models, we define and characterize asymptotic properties of the system such as eco‐evolutionary equilibria (including ESSs) and the convergence stability of these equilibria. For multistate structured populations, we introduce an ergodic flow preserving folding method for analysing such models.The methods developed in this paper for state‐structured populations and their extensions to multistate‐structured populations provide a simple way to create, analyse and simulate eco‐evolutionary dynamics in structured populations. Furthermore, their generality allows these techniques to be applied to a variety of problems in ecology and evolution.

A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation

As sensory and computing technology advances, multi-modal features have been playing a central role in ubiquitously representing patterns and phenomena for effective information analysis and recognition. As a result, multi-modal feature representation is becoming a progressively significant direction of academic research and real applications. Nevertheless, numerous challenges remain ahead, especially in the joint utilization of discriminatory representations and complementary representations from multi-modal features. In this article, a discriminant information theoretic learning (DITL) framework is proposed to address these challenges. By employing this proposed framework, the discrimination and complementation within the given multi-modal features are exploited jointly, resulting in a high-quality feature representation. According to characteristics of the DITL framework, the newly generated feature representation is further optimized, leading to lower computational complexity and improved system performance. To demonstrate the effectiveness and generality of DITL, we conducted experiments on several recognition examples, including both static cases, such as handwritten digit recognition, face recognition, and object recognition, and dynamic cases, such as video-based human emotion recognition and action recognition. The results show that the proposed framework outperforms state-of-the-art algorithms.

The impact of B2P electronic payroll and G2P digital welfare on formal financial inclusion in the global open economy

The objective of the present research is to assess how and to what extent government-to-person (G2P) digital welfare, and business-to-person (B2P) electronic payroll practices, affect formal financial inclusion outcomes in the global open economy. To this end, the paper relies on the inter-temporal economic theory of consumption, together with a random utility theoretic framework to characterize the behavioral manifestations of formal account ownership (FA), formal saving (FS) and borrowing (FB), attributable to G2P and B2P electronic transfer practices, across 155 economies between 2014, 2017 and 2021. Following sensitivity and robustness testing, the results highlight that ceteris-paribus, controlling for social, economic, financial, demographic, spatial as well as temporal influences, G2P digital welfare, and B2P electronic payroll practices significantly improve individuals’ likelihoods of formal financial inclusion in the global open economy. Indeed, while G2P digital welfare increases individuals’ marginal utilities (MUs) from FA, FS, and FB by 65.9%, 9.4% and 16.3% respectively; B2P electronic payroll practices increases individuals’ MUs from FA, FS, and FB by 41.2%, 22.3% and 18.1% respectively. Therefore, implications for real-time payment systems’ open innovation, as well as inclusive and sustainable growth are discussed.

Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detection

Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detection

A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts

The past few years have seen several works exploring learning economic solutions from data, including optimal auction design, function optimization, stable payoffs in cooperative games, and more. In this work, we provide a unified learning-theoretic methodology for modeling such problems and establish tools for determining whether a given solution concept can be efficiently learned from data. Our learning-theoretic framework generalizes a notion of function space dimension—the graph dimension—adapting it to the solution concept learning domain. We identify sufficient conditions for efficient solution learnability and show that results in existing works can be immediately derived using our methodology. Finally, we apply our methods in other economic domains, yielding learning variants of competitive equilibria and Condorcet winners.