TOWARD A THEORY OF SYSTEMS ENGINEERING

The derivation of a theory of systems engineering has long been complicated by the fact that there is little consensus within the systems engineering community regarding precisely what systems engineering is, what systems engineers do, and what might constitute reasonable systems engineering practices. To date, attempts at theories fail to accommodate even a sizable fraction of the current systems engineering community, and they fail to present a test of validity of systems theories, analytical methods, procedures or practices. This paper presents a more theoretical and more abstract approach to the derivation of a theory of systems engineering that has the potential to accommodate a broad segment of the systems engineering community and present a validity test. It is based on a simple preference statement: “I want the best system I can get.” From this statement, it is argued that a very rich theory can be obtained. Whereas most engineering disciplines are framed around a core set of widely accepted physical laws, to the authors' knowledge, this is the first attempt to frame an engineering discipline around a preference.

Social responsibility is the crucial factor in adopting early vaccination plans

Early vaccination of the general population is a very crucial aspect in the successful mitigation of highly infectious diseases, as it is the case of the SARS-CoV-2 pandemic. The perception of possible side-effects from early batches of vaccines, presumably under-tested, is often a hindering factor for people not in high-risk categories to optin for early vaccination. In this work, early vaccination is formulated under a game-theoretic view with preference ranking and expectation maximization, in order to explore the constraints and conditions under which individuals are keen to opt-in for getting vaccinated. Although simple preference ranking leads to purely non-cooperative / non-altruistic Nash equilibrium, stable cooperative strategies can emerge under simple constraints on the payoffs, specifically the individual cost from possible side-effects versus the collective gain for the community (‘herd’) when endorsing vaccination by default.Significance StatementIf the collective gain from community-scale vaccination is deemed even marginally greater than each individual’s cost of possible side-effects, then the best strategy is to ‘cooperate’, i.e., every individual to opt-in for getting vaccinated.This condition is independent from the probabilities of getting infected, with or without vaccination, and it is sufficient to lead to a stable cooperative Nash equilibrium.In any lethal infectious disease like SARS-CoV-2 and less-than-lethal possible side-effects from the vaccine, for anyone that is susceptible plus one more in his/her own close environment, vaccination is the optimal strategy for all.

Toward a Theory of Systems Engineering

The derivation of a theory of systems engineering has long been complicated by the fact that there is little consensus within the systems engineering community regarding precisely what systems engineering is, what systems engineers do, and what might constitute reasonable systems engineering practices. To date, attempts at theories fail to accommodate even a sizable fraction of the current systems engineering community, and they fail to present a test of validity of systems theories, analytical methods, procedures or practices. This paper presents a more theoretical and more abstract approach to the derivation of a theory of systems engineering that has the potential to accommodate a broad segment of the systems engineering community and present a validity test. It is based on a simple preference statement: “I want the best system I can get.” From this statement, it is argued that a very rich theory can be obtained. Whereas most engineering disciplines are framed around a core set of widely accepted physical laws, to the authors’ knowledge, this is the first attempt to frame an engineering discipline around a preference.

Applying a Multi-Criteria Project Portfolio Tool in Selecting Energy Peat Production Areas

This study demonstrates the characteristics of the new generic project portfolio selection tool YODA (“Your Own Decision Aid”). YODA does not include a mathematical aggregation model. Instead, the decision maker’s preferences are defined by the interactive articulation of acceptance thresholds of project-level decision criteria. Transparency and ease of adopting the method in participatory planning are sought using the method’s simple preference input. The characteristics of the YODA tool are introduced by presenting how it has been applied in participatory land use planning in northern Finland in selecting a combination of peat production sites to attain the goals defined at municipal level. In this process, each stakeholder first constructed a project portfolio that best met his or her preferences. In doing this, acceptance thresholds for project-level decision criteria were defined. In total, eight decision criteria were related to economic value, biodiversity, social impacts, and ecosystem services. Subsequently, the portfolios of different stakeholders were combined in line with the principles of robust portfolio modelling. Core projects were accepted by all stakeholders, while exterior projects were not accepted, and borderline projects by some of the stakeholders. Although the land use planning situation at hand was highly sensitive, because it was related to various aspects of sustainability, the use of YODA provided useful results. The first meeting with stakeholders identified 52 out of 99 sites that none of the stakeholders would use for energy peat production, due to their characteristics, whereas, in the second meeting, a smaller stakeholder group found 18 core projects and 26 borderline projects which could be potential areas for energy peat production. We conclude that YODA—as a generic project portfolio tool—can be used in various planning situations.

A Method to Ensure Preference Consistency in Multi-Attribute Selection Decisions

A number of approaches for multi-attribute selection decisions exist, each with certain advantages and disadvantages. One method that has recently been developed, called the hypothetical equivalents and inequivalents method (HEIM) supports a decision maker (DM) by implicitly determining the importances a DM places on attributes using a series of simple preference statements. In this and other multi-attribute selection methods, establishing consistent preferences is critical in order for a DM to be confident in his/her decision and its validity. In this paper, a general preference consistency method is developed, which is used to ensure that a consistent preference structure exists for a given DM. The method is demonstrated as part of HEIM, but is generalizable to any cardinal or ordinal preference structure, where the preferences can be over alternatives or attributes. These structures play an important role in making selection decisions in engineering design including selecting design concepts, materials, manufacturing processes, and configurations, among others. The theoretical foundations of the method are developed and the need for consistent preferences is illustrated in the application to a drill selection case study where the decision maker expresses inconsistent preferences.