Jose J. Padilla

Reference modelling in support of M&S—foundations and applications

Whether by design or by practice, systems engineering (SE) processes are used more and more often in Modeling and Simulation (M&S). While the two disciplines are very close, there are some differences that must be taken into account in order to successfully reuse practices from one community to another. In this paper, we introduce the M&S System Development Framework (MS-SDF) that unifies SE and M&S processes. The MS-SDF comprises the SE processes of requirements capture, conceptual modelling, and verification and validation (V&V), and extends them to M&S. We use model theory as a deductive apparatus in order to develop the MS-SDF. We discuss the benefits of the MS-SDF especially in the selection between federation development and multi-model approaches and the design of composable models and simulations. Lastly, a real life application example of the framework is provided.

System of Systems Engineering Requirements: Challenges and Guidelines

Abstract: Traditional systems engineering (SE) has been successful in developing requirements that are objective, verifiable, and definitive. These requirements are chiefly related to technical or technological issues necessary to achieve a desired level of system performance. In contrast, System of Systems Engineering (SoSE) engages a more complex and holistic problem space, including organizational, managerial, policy, human/social, and political dimensions that exist in conditions of emergence, ambiguity, and uncertainty; therefore, the traditional SE requirements paradigm must be called into question. At present, the SoSE requirements paradigm has not reached the level of maturity or sophistication experienced by traditional SE. It is a miscalculation to expect successful approaches for SE requirements development to enjoy the same level of success when applied directly to the SoSE problem domain. The purpose of this article is to explore the nature of requirements from an SoSE perspective. First, the article establishes a foundation for differences between the SE and SoSE problem domains. Second, the traditional SE paradigm governing requirements is developed. Third, the specific nature of the SoSE problem domain implications for requirements is established. Fourth, guidelines for requirements within SoSE efforts are provided. The article concludes with key implications for the development and use of requirements in the SoSE field by practitioners.

Ontology for modeling and simulation

This paper establishes what makes an ontology different in Modeling and Simulation (M&S) from other disciplines, vis-a-vis, the necessity to capture a conceptual model of a system in an explicit, unambiguous, and machine readable form. Unlike other disciplines where ontologies are used, such as Information Systems and Medicine, ontologies in M&S do not depart from a set of requirements but from a research question which is contingent on a modeler. Thus, the semiotic triangle is used to present that different implemented ontologies are representations of different conceptual models whose commonality depends on which research question is being asked. Ontologies can be applied to better capture the modelers perspective. The elicitation of ontological, epistemological, and teleological considerations is suggested. These considerations may lead to better differentiation between conceptualizations, which for a computer are of importance for use, reuse and composability of models and interoperability of simulations.

You Are What You Tweet: Connecting the Geographic Variation in America’s Obesity Rate to Twitter Content

We conduct a detailed investigation of the relationship among the obesity rate of urban areas and expressions of happiness, diet and physical activity on social media. We do so by analyzing a massive, geo-tagged data set comprising over 200 million words generated over the course of 2012 and 2013 on the social network service Twitter. Among many results, we show that areas with lower obesity rates: (1) have happier tweets and frequently discuss (2) food, particularly fruits and vegetables, and (3) physical activities of any intensity. Additionally, we provide evidence that each of these results offer different and unique insight into the variation of the obesity rate in urban areas within the United States. Our work shows how the contents of social media may potentially be used to estimate real-time, population-scale measures of factors related to obesity.

Epistemology of modeling and simulation

While ontology deals with the question of being or existence, epistemology deals with the question of gaining knowledge. This panel addresses the challenge of how we gain knowledge from modeling and simulation. What is the underlying philosophy of science of M&S? What are our canons of research for M&S? Is it sufficient to apply the foundational methods of the application domains, or do we need to address these questions from the standpoint of M&S as a discipline? The invited experts illuminate various facets from philosophical, mathematical, computational, and application viewpoints.

A multi-paradigm modeling framework for modeling and simulating problem situations

This paper proposes a multi-paradigm modeling framework (MPMF) for modeling and simulating problem situations (problems whose specification is not agreed upon). The MPMF allows for a different set of questions to be addressed from a problem situation than is possible through the use of a single modeling paradigm. The framework identifies different levels of granularity (macro, meso, and micro) from what is known and assumed about the problem situation. These levels of granularity are independently mapped to different modeling paradigms. These modeling paradigms are then combined to provide a comprehensive model and corresponding simulation of the problem situation. Finally, the MPMF is implemented to model and simulate the problem situation of representing the spread of obesity.

Toward a formalism of modeling and simulation using model theory

This article proposes a Modeling and Simulation (M&S) formalism using Model Theory. The article departs from the premise that M&S is the science that studies the nature of truth using models and simulations. Truth in models and simulations is relative as they seek to answer specific modeling questions. Consequently, truth in M&S is relative because every model is a purposeful abstraction of reality. We use Model Theory to express the proposed formalism because it is built from the premise that truth is relative. The proposed formalism allows us to: (1) deduce formal definitions and explanations of areas of study in M&S, including conceptual modeling, validity, and interoperability, and (2) gain insight into which tools can be used to semi-automate validation and interoperation processes.

Semiotics, entropy, and interoperability of simulation systems: mathematical foundations of M&S standardization

Semiotics identifies which symbols are used (syntax), what the meaning of these symbols is (semantics), and what the intention of using symbols is (pragmatics). These ideas have already been mapped to integratability of networks, interoperability of simulations, and composability of models for modeling and simulation applications. New research on model theory and algorithmic information theory support this viewpoint. Applying the finding of mathematics allows to define three different entropies: syntactical entropy that measures the variety of data representation, semantic entropy that measures the variety of data interpretation, and pragmatic entropy that measures the variety of data utilization. The paper shows the interconnection between these ideas and their implication for interoperability challenges: standards are needed on all levels to ensure meaningful interoperation, but their application reduces the interoperability space of federated solutions to the intersection of models, not to the union of models as often assumed in naïve approaches.

Model theoretic implications for agent languages in support of interoperability and composability

This paper evaluates the implications of model theory for agent languages. The tasks of ambassador agents are to represent simulations and identify potential contributions, select the best solutions in light of the question, compose the selected best solutions to provide the new functionality, and orchestrate their execution. Model-based data engineering can help to identify the information that needs to be exchanged between systems, existential and transformational dependencies can be identified using graph theory, and Petri nets can represent the availability of required information. All structures can be computed and fall under the realm of formal languages. Model theory is a subset of mathematics that focuses on the study of formal languages and their interpretations. Interpreting the terms model, simulation, and data of the modeling and simulation community using model theoretic terms allows the application of model theoretic insights. This allows to formally and unambiguously capture requirements for interoperability and composability.

Statistical Debugging for Simulations

Predictions from simulations have entered the mainstream of public policy and decision-making practices. Unfortunately, methods for gaining insight into faulty simulations outputs have not kept pace. Ideally, an insight gathering method would automatically identify the cause of a faulty output and explain to the simulation developer how to correct it. In the field of software engineering, this challenge has been addressed for general-purpose software through statistical debuggers. We present two research contributions, elastic predicates and many-valued labeling functions, that enable debuggers designed for general-purpose software to become more effective for simulations employing random variates and continuous numbers. Elastic predicates address deficiencies of existing debuggers related to continuous numbers, whereas many-valued labeling functions support the use of random variates. When used in combinations, these contributions allow a simulation developer tasked with localizing the program statement causing the faulty simulation output to examine 40% fewer statements than the leading alternatives. Our evaluation shows that elastic predicates and many-valued labeling functions maintain their ability to reduce the number of program statements that need to be examined under the imperfect conditions that developers experience in practice.