Formal Model Research Articles

A rationale for the Right-to-Development climate policy stance?

We present a formal model that analyzes the trade-offs between environmental policy and economic growth in a developing economy. The adoption of restrictive environmental policies limits the use of abundant fossil energy resources, which may slow down economic development and thus violate the Right-to-Development. If faster economic growth allows a country to grow out of pollution sooner, less stringent policies are good for growth and even for the environment, having adopted a long-term horizon. Accounting for a ceiling on cumulative emissions can reinforce the argument by providing an additional rationale to phase out pollution. One assumption is crucial for the argument to hold: polluting fossil energy is an essential input over the early phase of economic development, but not in the later phases. Such a discontinuity could result from structural change. We provide empirical evidence for the plausibility of a discontinuity in the elasticity of carbon dioxide emissions with respect to aggregate output, using cross country data, even if it does not appear to be as strong as assumed in the model economy.

Life Expectancy Reversals in Low‐Mortality Populations

AbstractBehind the steady march of progress toward longer life expectancy in many low‐mortality countries, there have been setbacks even before the Covid‐19 pandemic. In this paper, we use an exploratory approach to describe the temporal structure, age patterns, and geographic aspects of life expectancy reversals. We find that drops in life expectancy are often followed by larger than average improvements, which tells us that most reversals are transitory with little long‐term influence. The age structure of mortality decline when life expectancy falls is tilted toward older ages, a pattern that is quite different from the general pattern of mortality improvement. Geographic analysis shows that mortality reversals are correlated across neighboring countries like Italy and France, or Canada and the United States. These findings are consistent with contagious disease and weather being important causes of life expectancy reversals. We conclude with a discussion of implications for formal modeling and forecasting of mortality to accommodate these patterns that violate some standard assumptions.

S2O: A smart objects orchestrator framework based on the web of things

The Web of Things (WoT) is a new paradigm in which everyday objects are connected to the Internet using popular Web technologies. The smart things are abstracted into RESTful Web services to facilitate their manipulation. The composition of these objects within the same business process creates an automated, time-synchronized routine that can perform even the most complex tasks. BPEL is an orchestration language that defines the process responsible for coordinating the Web services involved. However, this language presents some limitations in this context. BPEL 2.0 does not support RESTful Web services; its specification is incompatible with this architectural style of services. Moreover, its temporal expressivity is insufficient to cover all the constraints that may arise when composing services. This work aims to adapt BPEL to the requirements of the WoT environment, enabling it to create processes that invoke the smart things in precise time intervals. The solution is to exploit one of BPEL's strengths: its extensibility. The BPEL specification is enriched with four activities that reflect the REST uniform interface. They include the necessary attributes to send the request to the target object and receive the response in convened format representation. Also, temporal attributes are added to BPEL elements to schedule their start, end, and duration of execution. The manual addition of these temporal values requires a verification of their accuracy. The BPEL process must be reviewed to ensure its validity before its execution. A temporal Petri Net is proposed to detect any conflicts or inconsistencies between BPEL activities. As a result, associating the formal model with the extension allows BPEL to orchestrate smart things represented by RESTful Web services according to a well-defined temporal scenario. They respect the REST constraints and provide the BPEL activities with temporal attributes for efficient time management. The approach can be applied in all application areas to create temporal scenarios.

An Axiomatic Theory for Reversible Computation

Undoing computations of a concurrent system is beneficial in many situations, such as in reversible debugging of multi-threaded programs and in recovery from errors due to optimistic execution in parallel discrete event simulation. A number of approaches have been proposed for how to reverse formal models of concurrent computation, including process calculi such as CCS, languages like Erlang, and abstract models such as prime event structures and occurrence nets. However, it has not been settled as to what properties a reversible system should enjoy, nor how the various properties that have been suggested, such as the parabolic lemma and the causal-consistency property, are related. We contribute to a solution to these issues by using a generic labelled transition system equipped with a relation capturing whether transitions are independent to explore the implications between various reversibility properties. In particular, we show how all properties we consider are derivable from a set of axioms. Our intention is that when establishing properties of some formalism, it will be easier to verify the axioms rather than proving properties such as the parabolic lemma directly. We also introduce two new properties related to causal-consistent reversibility, namely causal liveness and causal safety, stating, respectively, that an action can be undone if (causal liveness) and only if (causal safety) it is independent from all of the following actions. These properties come in three flavours: defined in terms of independent transitions, independent events, or via an ordering on events. Both causal liveness and causal safety are derivable from our axioms.

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

Theoretical investigation of the conformational preference and spectroscopic analysis of cinnamoyl chloride and cinnamide

The rotational isomers of cinnamoyl chloride and cinnamide have been computed using the B3LYP method and the 6-311++G (d, p) basis set to calculate their molecular structure, conformational stability, and spectroscopic properties. Computations revealed that the s-cis rotamers of both compounds are the most stable rotamers in both the gas phase and in the solution. The s-trans rotational barrier is 8.76 kcal/mol and 5.63 kcal/mol for cinnamoyl chloride and cinnamide, respectively. Replacing the Cl group of the cinnamoyl chloride with the NH2 group in the cinnamide has reduced the highest occupied molecular orbital – lowest unoccupied molecular orbital (HOMO-LUMO) gap. The solvation effects on the conformational stability of rotamers have been studied in nine solvents (heptane, chloroform, tetrahydrofuran, dichloroethane, acetone, ethanol, methanol, dimethyl sulfoxide, and water) using the integral equation formalism of the polarizable continuum model. The chemical shifts of 13C and 1H NMR spectra have been simulated in the gas phase, dimethyl sulfoxide (DMSO), and chloroform using the gauge-independent atomic orbital (GIAO) method. The UV absorption spectral analysis has been computed in different solvents (chloroform, methanol, and water), and atomic charges calculated. The examination of vibrational wave numbers and their corresponding assignments revealed excellent agreement between simulated and experimental infrared spectra for the studied molecules.

Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows

The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.

Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows

The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.

Using Large Language Models for (De-)Formalization and Natural Argumentation Exercises for Beginner's Students

Using Large Language Models for (De-)Formalization and Natural Argumentation Exercises for Beginner's Students

When manufacturers turn into digital platform providers: A transformation model to understand the platformization pathway

Platformization has arrived in manufacturing. However, to enter the digital platform business manufacturers need to make a fundamental shift from product thinking to platform thinking. To master this shift, firms have to take the platformization pathway, which describes the process when manufacturers turn into digital platform providers. However, little is known about platformization on the firm level from a transformation perspective. Our study attempts to close this gap and develops a transformation model that frames the platformization pathway when established manufacturers become platform leaders. For this purpose, we adopt the concept of ambidexterity and develop a theoretical framework to conceptualize and investigate the transformation process as a result of sequential transitions and balancing between explorative and exploitative activities. Moreover, we use interviews from 29 large manufacturers and conduct an inductive case study analysis to identify the different stages, the contextual factors and the dynamics of transformation. By combining the theoretical framework and the results of our case study analysis, we develop a formal transformation model that contributes to understanding the manufacturers' platformization pathway. Our results also aim to support manufacturers in designing their strategies so they can turn into leading digital platform providers.

Enhanced performance prediction of ATL model transformations

Model transformation languages are domain-specific languages used to define transformations of models. These transformations consist of the translation from one modeling formalism into another or just the updating of a given model. Such transformations are often described declaratively and are often implemented based on very small models that cover the language of the input model. As a result, transformation developers are often unable to assess the time required to transform a larger model.Hence, we propose a prediction approach based on machine learning which uses a set of model characteristics as input and provides a prediction of the execution time of a transformation defined in the Atlas Transformation Language (ATL). In our previous work (Groner et al., 2023), we already showed that support vector regression in combination with a model characterization based on the number of model elements, the number of references, and the number of attributes is the best choice in terms of usability and prediction accuracy for the transformations considered in our experiments.A major weakness of our previous approach is that it fails to predict the performance of transformations that also transform attribute values of arbitrary length, such as string values. Therefore, we investigate in this work whether an extension of our feature sets that describes the average size of string attributes can help to overcome this weakness.Our results show that the random forest approach in combination with model characterizations based on the number of model elements, the number of references, the number of attributes, and the average size of string attributes filtered by the 85th percentile of their variance is the best choice in terms of the simple way to describe a model and the quality of the obtained prediction. With this combination, we obtained a mean absolute percentage error (MAPE) of 5.07% over all modules and a MAPE of 4.82% over all modules excluding the transformation for which our previous approach failed. Whereas, we obtained previously a MAPE of 38.48% over all modules and a MAPE of 4.45% over all modules excluding the transformation for which our previous approach failed.

Differences in perceived sources of uncertainty in natural hazards science advice: lessons for cross-disciplinary communication

IntroductionWe conducted mental model interviews in Aotearoa NZ to understand perspectives of uncertainty associated with natural hazards science. Such science contains many layers of interacting uncertainties, and varied understandings about what these are and where they come from creates communication challenges, impacting the trust in, and use of, science. To improve effective communication, it is thus crucial to understand the many diverse perspectives of scientific uncertainty.MethodsParticipants included hazard scientists (n = 11, e.g., geophysical, social, and other sciences), professionals with some scientific training (n = 10, e.g., planners, policy analysts, emergency managers), and lay public participants with no advanced training in science (n = 10, e.g., journalism, history, administration, art, or other domains). We present a comparative analysis of the mental model maps produced by participants, considering individuals’ levels of training and expertise in, and experience of, science.ResultsA qualitative comparison identified increasing map organization with science literacy, suggesting greater science training in, experience with, or expertise in, science results in a more organized and structured mental model of uncertainty. There were also language differences, with lay public participants focused more on perceptions of control and safety, while scientists focused on formal models of risk and likelihood.DiscussionThese findings are presented to enhance hazard, risk, and science communication. It is important to also identify ways to understand the tacit knowledge individuals already hold which may influence their interpretation of a message. The interview methodology we present here could also be adapted to understand different perspectives in participatory and co-development research.

A Formal Model of Affiliative Interpersonality

Capturing the complexity of interpersonal dynamics—emerging from approach and avoidance motives of two individuals in dyadic interplay—remains challenging. In line with calls for embracing complexity in psychological research using formal modeling, we employed evolutionary game theory to investigate the underlying mechanisms of affiliative interpersonality. We constructed a relational state space that represents the ways of relating available in the momentary state of an interpersonal relationship. Next, we modeled relationships as trajectories in that relational space. Qualitatively different interpersonal dynamics emerged: (a) global stability with only one relational attractor (e.g., pure reciprocal friendliness), (b) bistability with two mutually exclusive attractors (e.g., either pure friendliness or pure distance), and (c) cycles between friendliness and distance in the relational space. The bistable dynamics appear to resemble the phenomenon of interpersonal complementarity (e.g., friendliness invites friendliness). Furthermore, the model generates psychopathologically relevant dynamics (e.g., oscillating, unstable interpersonal relationships in borderline personality disorder).

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution.

Integrating healthcare into traffic accident prevention through predictive modeling holds immense potential. Decentralized Defense presents a transformative vision for combating cyberbullying, prioritizing user privacy, fostering a safer online environment, and offering valuable insights for both healthcare and predictive modeling applications. As cyberbullying proliferates in social media, a pressing need exists for a robust and innovative solution that ensures user safety in the cyberspace. This paper aims toward introducing the approach of merging Blockchain and Federated Learning (FL), to create a decentralized AI solutions for cyberbullying. It has also used Alloy Language for formal modeling of social connections using specific declarations that are defined by the novel algorithm in the paper on two different datasets on Cyberbullying and are available online. The proposed novel method uses DBN to run established relation tests amongst the features in two phases, the first is LSTM to run tests to develop established features for the DBN layer and second is that these are run on various blocks of information of the blockchain. The performance of our proposed research is compared with the previous research and are evaluated using several metrics on creating the standard benchmarks for real world applications.

Does Consumer’s Happiness and Other Emotions Signaling Affect Seller’s Prices? Theory and Evidence From Six Field Studies

The factors that determine the prices of goods and services are within the core inquiry of economic science. Do consumer’s emotions affect seller’s selling prices? The current study explores this issue through six field studies. The first four studies focus on happiness, demonstrating for the first time that happiness signals affect the prices of products and services. Happy customers are offered to pay lower price for goods–cellphones and bicycles–and for related services. The results are relevant both in face-to-face and online interactions and in between—and within—subject designs, implying that extended real income is another objective benefit of individual happiness. Two additional experiments do not show the same effect when other emotions–anger and disgust–are signaled by the consumer. We present a formal model for the positive happiness premium and discuss the welfare implications of our findings. JEL classification: C93, D01, D21.

A comment on Powell and formal models of power sharing

Ruling elites commonly concede institutional reforms such as expanding the franchise. In existing models, sharing power in this manner enables ruling elites to credibly commit to perpetual redistribution. In ‘Power Sharing with Weak Institutions,’ Powell (2024) explains why the commitment problem runs deeper: When institutions are weak, elites are likely to block the implementation of promised institutional concessions. I provide new insights into three foundational premises of Powell (2024) and related models. First, I identify a necessary condition for a common result: Ruling elites always minimize permanent power-sharing concessions vis-à-vis temporary concessions, subject to preventing revolt. However, unless reforming institutions is somehow costly, these two tools are perfect substitutes. Second, I discuss how to conceptualize institutional strength within this class of models. Third, in weak institutional environments, I suggest how scholars can model credible commitments to share power or democratize.

Correction: A framework for embedded software portability and verification: from formal models to low-level code

Correction: A framework for embedded software portability and verification: from formal models to low-level code

A bio-inspired model for robust navigation assistive devices

This paper proposes a new implementation and evaluation in a real-world environment of a bio-inspired predictive navigation model for mobility control, suitable especially for assistance of visually impaired people and autonomous mobile systems. This bio-inspired model relies on the interactions between formal models of three types of neurons identified in the mammals’ brain implied in navigation tasks, namely place cells, grid cells, and head direction cells, to construct a topological model of the environment under the form of a decentralized navigation graph. Previously tested in virtual environments, this model demonstrated a high tolerance to motion drift, making possible to map large environments without the need to correct it to handle such drifts, and robustness to environment changes. The presented implementation is based on a stereoscopic camera, and is evaluated on its possibilities to map and guide a person or an autonomous mobile robot in an unknown real environment. The evaluation results confirm the effectiveness of the proposed bio-inspired navigation model to build a path map, localize and guide a person through this path. The model predictions remain robust to environment changes, and allow to estimate traveled distances with an error rate below 3% over test paths, up to 100m. The tests performed on a robotic platform also demonstrated the pertinence of navigation data produced by this navigation model to guide an autonomous system. These results open the way toward efficient wearable assistive devices for visually impaired people independent navigation.

A Theory of Minority Entrepreneurship

Since different racial and ethnic groups experience different entrepreneurial outcomes, we need to develop an explanation for these differences as a foundation for overcoming them. Early analyses of entrepreneurship by Cantillon, Turgot, Smith, Say are applied to problems of minority entrepreneurship. Menger, Schumpeter, and Kirzner provided the most modern and complete treatments, but entrepreneurship’s multifaceted nature makes it difficult to incorporate into formal models. Segmented markets and preferred habitat theories drawn from financial economics are applied to address special issues confronting minority entrepreneurs and suggest how they might inform public policy. Because policy implications are suggested, it is hoped that this paper may contribute to a constructive conversation on opportunities to encourage entrepreneurship and advance minority communities.

Random finite noncommutative geometries and topological recursion

In this paper, we investigate a model for quantum gravity on finite noncommutative spaces using the theory of blobbed topological recursion. The model is based on a particular class of random finite real spectral triples {(\mathcal{A}, \mathcal{H}, D, \gamma, J)} , called random matrix geometries of type {(1,0)} , with a fixed fermion space {(\mathcal{A}, \mathcal{H}, \gamma, J)} and a distribution of the form {e^{- \mathcal{S} (D)}\, {\mathrm{d}} D} over the moduli space of Dirac operators. The action functional {\mathcal{S} (D)} is considered to be a sum of terms of the form {\prod_{i=1}^s \mathrm{Tr} ({D^{n_i}})} for arbitrary {s \geqslant 1} . The Schwinger–Dyson equations satisfied by the connected correlators {W_n} of the corresponding multi-trace formal 1-Hermitian matrix model are derived by a differential geometric approach. It is shown that the coefficients {W_{g,n}} of the large N expansion of {W_n} ’s enumerate discrete surfaces, called stuffed maps, whose building blocks are of particular topologies. The spectral curve {( {\Sigma, \omega_{0,1}, \omega_{0,2}} )} of the model is investigated in detail. In particular, we derive an explicit expression for the fundamental symmetric bidifferential {\omega_{0,2}} in terms of the formal parameters of the model.