Formal Model Research Articles

On the influence of conventional and automated market makers on market quality in cryptoeconomic systems

Decentralized exchanges (DEXs) have become an alternative to centralized exchanges (CEXs) for trading assets in the form of tokens in cryptoeconomic system markets. The emergence of DEXs is strongly driven by their potential to tackle challenges for market quality originating from CEXs by design, such as opaque market-making strategies and centralization of power. However, it remains unclear to what extent DEXs can enhance market quality compared to CEXs. A core reason for this is the lack of an analysis concept for investigating influences of market makers, including automated market makers (AMMs) used in DEXs and conventional market makers used in CEXs, on market quality in cryptoeconomic systems. To better understand influences of market makers on market quality in cryptoeconomic systems, we developed an analysis concept based on our formal price model grounded in established concepts of market microstructure. We demonstrate the usefulness of the analysis concept by examining conventional market makers on CEXs (i.e., Binance and Coinbase) and automated market makers (AMMs) on DEXs (i.e., Uniswap v2 and Uniswap v3). The main purpose of this work is to support the analysis of influences of different market makers on market quality in cryptoeconomic systems. This is useful to better understand how cryptoeconomic systems can ensure high market quality and safeguard market participants, when issuing tokens.

Indentation and reading time: a randomized control trial on the differences between generated indented and non-indented if-statements

Indentation is an old technique that emphasizes elements in source code using white spaces or tabs. But while this technique has been taught and applied for decades, evidence for its effectiveness is weak: up to 2022 relatively few experiments can be found – the present authors are only aware of one single experiment that revealed an effect of indentation and reported the effect size, but even in that experiment the effect of indentation was found to be weak. The situation changed recently, where an experiment was published that suddenly revealed a strong and large effect of indentation in control flows on reaction time. However, although the experiment provided an initial indication of a possible cause for the difference between indented and non-indented code (the length of the code that can be skipped in indented code), the evidence for this indicator was rather weak. The present paper presents a formal model on the differences in reading times (measured in terms of reaction times) between indented and non-indented code. Based on that model a controlled experiment on generated tasks was designed and executed on 27 participants (undergraduate students, PhD students, professionals). The experiment (again) confirms a strong (p < .001) and large (ηp2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta _{p}^{2}$$\\end{document} = .198, MNon-IndentedMIndented\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{M_{Non-Indented}}{M_{Indented}}$$\\end{document} = 2.13) effect of indentation. Furthermore, it confirms that the larger the skippable code is, the larger is the difference between reading time of indented and non-indented code (p = .001, ηp2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta _{p}^{2}$$\\end{document} = .072). I.e., the experiment goes beyond the point “indented vs non-indented code” and explains the difference by revealing a factor that controls this difference. However, although the previous statements holds true for the whole sample in the experiment, this effect could only be shown for a subset of individual participants.

Guilt and Guilty Pleas

Plea bargaining figures heavily in criminal justice systems in the United States and, increasingly, around the globe. Conventional wisdom holds that plea bargaining generates efficiency gains for all parties, while sorting the guilty from the innocent. We build a series of formal models to consider the relationship between a defendant’s guilt and her likelihood of pleading guilty. In an inversion of the conventional wisdom, we show that under a range of empirically plausible scenarios—for example, if criminals are more risk-seeking than the wrongfully accused, or if prosecutors derive a career benefit from trial wins—the innocent are more likely than the guilty to plea bargain.

Formal Modeling and Verification of Lycklama and Hadzilacos’s Mutual Exclusion Algorithm

This study describes our thorough experience of formal modeling and exhaustive verification of concurrent systems, particularly mutual exclusion algorithms. The experience focuses on Lycklama and Hadzilacos’s (LH) mutual exclusion algorithm. LH rests on the reduced size of the shared state, contains a mechanism that tries to enforce an FCFS order to processes entering their critical section, and embodies Burns and Lamport’s (BL) mutual exclusion algorithm. The modeling methodology is based on timed automata and the model checker of the popular Uppaal toolbox. The effectiveness of the modeling and analysis approach is first demonstrated by studying the BL’s solution and retrieving all its properties, including, in general, its unbounded overtaking, which is the non-limited number of by-passes a process can suffer before accessing its critical section. Then, the LH algorithm is investigated in depth by showing it fulfills all the mutual exclusion properties when it operates with atomic memory. However, as this study demonstrates, LH is not free of deadlocks when used with non-atomic memory. Finally, a state-of-the-art mutual exclusion solution is proposed, which relies on a stripped-down LH version for processes, which is used as the arbitration unit in a tournament tree (TT) organization. This study documents that LH’s TT-based algorithm satisfies all the mutual exclusion properties, with a linear overtaking, both using atomic and non-atomic memory.

How to Formalize Different Types of Norms in Multi-agent Systems: A Methodology Focused on the T-Norm Model

In a world where many activities are carried out digitally, it is increasingly urgent to be able to formally represent the norms, policies, and contracts that regulate these activities in order to make them understandable and processable by machine. In multi-agent systems, the process to be followed by a person to choose a formal model of norms and transform a norm written in a natural language into a formal one by using the selected model is a demanding task. In this paper, we introduce a methodology to be followed by people to understand the fundamental elements that they should consider for this transformation. We will focus mainly on a methodology for formalizing norms using the T-Norm model, this is because it allows us to express a rich set of different types of norms. Nevertheless, the proposed methodology is general enough to also be used, in some of its steps, to formalize norms using other formal languages. In the definition of the methodology, we will explicitly state which types of norms can be expressed with a given model and which cannot. Since there is not yet a set of different types of norms that is sufficiently expressive and is recognized as valid by the Normative Mutiagent Systems (NorMAS) community, another goal of this paper is to propose and discuss a rich set of norms types that could be used to study the expressive power of different formal models of norms, to compare them, and to translate norms formalized with one language into norms written in another language.

Visual working memory for natural scenes: challenges and opportunities.

Visual working memory is a fundamental cognitive process that people use thousands of times each day as they engage in visually guided behavior. Thus, it is important to understand how the natural visual input-which consists of complex, spatially organized, continuously varying features-is represented in visual working memory. However, most research has used arrays of discrete, artificial objects defined by simple features, and existing formal models of visual working memory cannot be applied to natural scenes. In this paper, we identify 3 key aspects of natural scenes that are not captured by existing formal models of visual working memory, along with 2 distinct types of attention that must be considered. The goal is to clearly define the challenges and opportunities for moving models of visual working memory from arrays of artificial objects to natural scenes.

"Leap before you look": Conditions that suppress explicit, knowledge-based learning during visuomotor adaptation.

When learning a novel visuomotor mapping (e.g., mirror writing), accuracy can improve quickly through explicit, knowledge-based learning (e.g., aim left to go right), but after practice, implicit or procedural learning takes over, producing fast, natural movements. This procedural learning occurs automatically, whereas it has recently been found that knowledge-based learning can be suppressed by the gradual introduction of the novel mapping when participants must make fast movements and visuomotor perturbations are small (e.g., 30° rotations). We explored the range of task instructions, perturbation parameters, and feedback that preclude or encourage this suppression. Using a reaching task with a rotation between screen position and movement direction, we found that knowledge-based learning could be suppressed even for an extreme 90° rotation, but only if it was introduced gradually and only under instructions to move quickly. If the rotation was introduced abruptly or if instructions emphasized accuracy over speed, knowledge-based learning occurred. A second experiment indicated that knowledge-based learning always occurred in the absence of continuous motion feedback, evidenced by the time course of learning, the aftereffects of learning when the rotation was abruptly removed, and the outcome of formal model comparison between a dual-state (procedural and knowledge-based) versus a single-state (procedural only) learning model of the data. A third experiment replicated the findings and verified that the knowledge-based component of the dual-state model corresponded to explicit aiming, whereas the procedural component was slow to unlearn. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Guidelines for mechanistic modeling and analysis in cardiovascular research.

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

A numerical study of gravity-driven instability in strongly coupled dusty plasma. Part 3. Homo-interaction between a pair of rising/falling bubbles/droplets

A numerical study of the homo-interactions between two falling droplets and between two rising bubbles in a strongly coupled dusty plasma medium is presented in this article. The strongly coupled dusty plasma is considered as a viscoelastic fluid using the generalized hydrodynamic fluid model formalism. Two factors that affect homo-interactions are taken into account: the initial spacing and the coupling strength of the medium. Three different spacings between two droplets are simulated: widely, medium and closely. In each case, the coupling strength has been given as mild–strong and strong. It is shown that the overall dynamic is governed by the competition between the acceleration of two droplets/bubbles due to gravity and the interaction due to the closeness of the droplets/bubbles. Particularly in viscoelastic fluids, apart from the initial separation, shear waves originating from rotating vortices are responsible for the closeness of two droplets or bubbles. Several two-dimensional simulations have been carried out. This work is a continuation of the work done in Parts 1 (Dharodi &amp; Das, J. Plasma Phys., vol. 87, issue 2, 2021, 905870216) and 2 (Dharodi, J. Plasma Phys., vol. 87, issue 4, 2021, 905870402).

A Systematic Review of Teleophthalmology Services Post-COVID-19 Pandemic in New Zealand, the United Kingdom, Australia, the United States of America, and Canada.

Background: This systematic review of teleophthalmology services in Australia, the United States of America (USA), Canada, and the United Kingdom (UK) during the COVID-19 pandemic is aimed to evaluate changes in teleophthalmology, comparing them to New Zealand (NZ). Methods: A literature search of electronic databases Scopus, Proquest, PubMed, Embase, Web of Science, Cochrane Library, Google Scholar, and Google was conducted using search terms: telemedicine, ophthalmology, teleophthalmology/teleophthalmology, and COVID/COVID-19/coronavirus/covid-pandemic. Studies describing teleophthalmology services created in response to COVID-19 restrictions from March 1, 2020, to January 31, 2024, were analyzed. Results: Of the articles, 37 describing 29 discrete teleophthalmology services were included. There were 15 services in the USA, seven in the UK, two in Canada, two in Australia, and three in NZ. The models of care in the USA were well described, and teleophthalmology was used for general, external, anterior segment, neuro-ophthalmology, and oculoplastic consults, as well as for grading of fundus images in the emergency department setting. In the UK, teleophthalmology was used for general eye care, oculoplastics, and pediatric ophthalmology. In Australia, teleophthalmology was used for postglaucoma surgery monitoring of Intraocular Pressure. In NZ, teleophthalmology was used for general eye consults and triaging, but no formal models were described. Conclusion: COVID-19 offered a unique opportunity for re-examination and expansion of teleophthalmology services globally. Video-based and home-screening teleophthalmology services are feasible but have limitations. Investing in multidisciplinary and community-based technology partnerships can create more equitable teleophthalmology care models (to complement and, when necessary, replace traditional in-person consults), within existing frameworks, making eye care more accessible and efficient.

Applying a hierarchical Bayesian framework to reveal how fear and animal ownership drive human's valuation of and interactions with coyotes

AbstractHuman dimensions research is valuable to managing human‐wildlife interactions, especially in urban environments where such interactions are common. Survey data, which commonly contain Likert scales and questions, are useful in this field; however, these data can be difficult to analyze with formal modeling approaches. We demonstrate one approach, based on hierarchical Bayesian ordinal regression, to evaluate human‐coyote relationships in Rhode Island, USA. We implemented a survey to collect demographic and sociocultural characteristics of Rhode Island residents and information related to their knowledge of and experiences with coyotes. Our objectives were to assess how these characteristics affected respondents' valuation of and interactions (sightings and incidents) with coyotes. We analyzed 980 surveys from October to December 2020. We found that respondents who had fear of coyotes or experienced an incident between an owned animal and coyote, had the lowest valuation of coyotes. The same demographic of respondents also reported the highest sightings of and incidents with coyote. These results indicate that fearful residents, in addition to pet and livestock owners, are priority targets for disseminating information or programming about coyotes. Our analyses and findings demonstrate how Bayesian ordinal regression can provide clear and appropriate inference from survey data on how groups of people vary in their relationship with wildlife. These results are important in effectively and efficiently allocating resources towards mitigation, education, and management of human‐wildlife interactions.

An abstract and structured account of dialectical argument strength

This paper presents a formal model of dialectical argument strength in terms of the number of ways in which an argument can be successfully attacked in expansions of an abstract argumentation framework. First a model is proposed that is abstract but designed to avoid overly limiting assumptions on instantiations or dialogue contexts. It is then shown that most principles for argument strength proposed in the literature fail to hold for the proposed notions of dialectical strength, which clarifies the rational foundations of these principles and highlights the importance of distinguishing between kinds of argument strength, in particular logical, dialectical and rhetorical argument strength. The abstract model is then instantiated with ASPIC+ to test the claim that it does not make overly limiting assumptions on the structure of arguments and the nature of their relations.

Formal Theories of Special Interest Influence

The impact of money on politics—whether through campaign finance, lobbying, or independent expenditure—raises key normative questions about democratic representation and accountability. In recent years there has been a deluge of new data allowing researchers to study money in politics from new and exciting perspectives. As a complement to this trend, there has also been a resurgence of interest in developing formal models to provide new theoretical insights that can help us understand how, why, and when money influences politics and policy. We review several major themes in this literature, focusing specifically on exchange-based models, informational models, and subsidy-based models. We compare and contrast the key contributions, and limitations, of each approach in understanding the role of lobbying and campaign finance in politics. We also discuss future avenues for research that incorporate aspects of each approach, which, we believe, will introduce new theoretical insights for understanding special interest influence.

The Whys and Hows of Theory—Comparing Cognitive Science and Economics

AbstractGiven the ongoing debates about the replication crisis, theory crisis, and cooperation among disciplines in cognitive science, it is instructive to compare cognitive science with economics. The two fields face common challenges, most importantly in that both study complex, open systems. The strategies for facing these challenges, however, are quite different. Economics was long dominated by theory. Cognitive science takes a multidisciplinary approach, and despite its attendant diversity is dominated by psychology, which itself often neglects theory. I defend economics’ use of theory, characterizing its formal modeling tradition as an effective divide-and-conquer strategy for understanding complex, open systems. I argue that theory and experimentation ideally support one another, making replicability less of an issue. I also discuss the appropriate level(s) of analysis in economics and cognitive science as products of the systems they study. Finally, I compare the two fields’ very different community structures, treating economics as a cautionary tale and advocating pluralism.

Support for the Time-Varying Drift Rate Model of Perceptual Discrimination in Dynamic and Static Noise Using Bayesian Model-Fitting Methodology.

The drift-diffusion model (DDM) is a common approach to understanding human decision making. It considers decision making as accumulation of evidence about visual stimuli until sufficient evidence is reached to make a decision (decision boundary). Recently, Smith and colleagues proposed an extension of DDM, the time-varying DDM (TV-DDM). Here, the standard simplification that evidence accumulation operates on a fully formed representation of perceptual information is replaced with a perceptual integration stage modulating evidence accumulation. They suggested that this model particularly captures decision making regarding stimuli with dynamic noise. We tested this new model in two studies by using Bayesian parameter estimation and model comparison with marginal likelihoods. The first study replicated Smith and colleagues' findings by utilizing the classical random-dot kinomatogram (RDK) task, which requires judging the motion direction of randomly moving dots (motion discrimination task). In the second study, we used a novel type of stimulus designed to be like RDKs but with randomized hue of stationary dots (color discrimination task). This study also found TV-DDM to be superior, suggesting that perceptual integration is also relevant for static noise possibly where integration over space is required. We also found support for within-trial changes in decision boundaries ("collapsing boundaries"). Interestingly, and in contrast to most studies, the boundaries increased with increasing task difficulty (amount of noise). Future studies will need to test this finding in a formal model.

Formal models in normative political theory

This article revisits the conventional distinction in political science between ‘positive’ and ‘normative’ political theory, particularly the belief that formal and mathematical methods are only pertinent to positive political theory. We argue that formal models are equally valuable in normative political theory for three reasons: they can make thought experiments more rigorous, they can demonstrate the consistency of normative principles, and they can provide insights into the practical workings of novel institutional arrangements in the absence of empirical data. The integration of formal models into normative political theory presents challenges, including the development of criteria for evaluating these models and potential shifts in research focus. Integration can also strengthen political theory’s tenuous role in the political science discipline.

Development and Synthesis of Linguistic Models for Catalytic Cracking Unit in a Fuzzy Environment

This research develops a method for synthesizing linguistic models of fuzzy systems with fuzzy input and output parameters that are described by linguistic variables. Based on the proposed method, linguistic models of the Title 1000 catalytic cracking unit for heavy residues at the Shymkent oil refinery are developed, describing the dependence of the volume and quality of gasoline on the input and operating parameters of the facility, which are fuzzy. It is substantiated that the use of a fuzzy approach, which allows the use of the experience, knowledge, and intuition (intelligence) of the decision maker and subject matter experts, is the most suitable effective method for synthesizing models of complex, fuzzily described objects and processes for comparison with other methods. The main idea of the proposed work is to solve the problems of shortage and fuzziness of initial information when developing models and optimizing the operating modes of a catalytic cracking unit through the use of knowledge, experience, and intuition of experts in this field. To solve the problems of the shortage of initial quantitative information and the fuzziness of available information when developing mathematical models, it is proposed to systematically use statistical methods, expert assessment methods, and a heuristic method based on fuzzy logic. The scientific novelty of the research lies in the development of a method for synthesizing linguistic models in a fuzzy environment and an algorithm for its implementation, which makes it possible to describe the dependence of the fuzzy values of the object’s output parameters on its fuzzy input and operating parameters. The proposed approach allows the formalization and synthesis of models of fuzzily described objects when other methods of model development are not applicable or do not give the expected results. The results of the work were simulated in the MATLAB Fuzzy Logic Toolbox.

A causal modeler's guide to double effect reasoning

AbstractTrolley problems and like cases are often thought to show the inadequacy of purely consequentialist moral theories. In particular, they are often taken to reveal that consequentialists unduly neglect the moral significance of the causal structure of decision problems. To precisify such critiques and one sort of deontological morality they motivate, I develop a formal modeling framework within which trolley problems can be represented as suitably supplemented structural causal models and various consequentialist and double effect‐inspired moral theories can be viewed as disagreeing over the inputs of a common decision rule.

MSCFS-RP: A Colored-Petri-Net-Based Analysis Model for Master–Slave Cloud File Systems with Replication Pipelining

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems.

Productive explanation: A framework for evaluating explanations in psychological science.

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).