Efficient Decision Process Research Articles

The fine line between automation and augmentation in website usability evaluation

Artificial Intelligence (AI) systems are becoming widespread in all aspects of society, bringing benefits to the whole economy. There is a growing understanding of the potential benefits and risks of this type of technology. While the benefits are more efficient decision processes and industrial productivity, the risks may include a potential progressive disengagement of human beings in crucial aspects of decision-making. In this respect, a new perspective is emerging that aims at reconsidering the centrality of human beings while reaping the benefits of AI systems to augment rather than replace professional skills: Human-Centred AI (HCAI) is a novel framework that posits that high levels of human control do not contradict high levels of computer automation. In this paper, we investigate the two antipodes, automation vs augmentation, in the context of website usability evaluation. Specifically, we have analyzed whether the level of automation provided by a tool for semi-automatic usability evaluation can support evaluators in identifying usability problems. Three different visualizations, each one corresponding to a different level of automation, ranging from a full-automation approach to an augmentation approach, were compared in an experimental study. We found that a fully automated approach could help evaluators detect a significant number of medium and high-severity usability problems, which are the most critical in a software system; however, it also emerged that it was possible to detect more low-severity usability problems using one of the augmented approaches proposed in this paper.

Generalized Neutrosophic Soft Expert Set for Multiple-Criteria Decision-Making

Smarandache defined a neutrosophic set to handle problems involving incompleteness, indeterminacy, and awareness of inconsistency knowledge, and have further developed it neutrosophic soft expert sets. In this paper, this concept is further expanded to generalized neutrosophic soft expert set (GNSES). We then define its basic operations of complement, union, intersection, AND, OR, and study some related properties, with supporting proofs. Subsequently, we define a GNSES-aggregation operator to construct an algorithm for a GNSES decision-making method, which allows for a more efficient decision process. Finally, we apply the algorithm to a decision-making problem, to illustrate the effectiveness and practicality of the proposed concept. A comparative analysis with existing methods is done and the result affirms the flexibility and precision of our proposed method.

NEUTROSOPHIC PARAMETRIZED SOFT SET THEORY AND ITS DECISION MAKING

In this work, we present definition of neutrosophic parameterized (NP) soft set and its operations. Then we define NP-aggregation operator to form NP-soft decision making method which allows constructing more efficient decision processes. We also dive an example which shows that they can be successfully applied to problem that contain indeterminacy.

Neutrosophic Parametrized Soft Set Theory and its Decision Making

In this work, we present definition of neutrosophic parameterized (NP) soft set and its operations. Then we define NP-aggregation operator to form NP-soft decision making method which allows constructing more efficient decision processes. We also dive an example which shows that they can be successfully applied to problem that contain indeterminacy.

Advancing Collective Decision-Making Theory with Integrated Agent-Based Modeling and Ethnographic Data Analysis: An Example in Ecological Restoration

Ecological restoration actions generally result from collective decision-making processes and can involve diverse, at times contentious, views. As such, it is critical to understand these processes and the factors that might influence the resolution of diverse perspectives into a set of coordinated actions. This paper describes the adaptation and calibration of a stylized collective decision-making agent-based model using ethnographic data, to advance theory on how decisions emerge in the context of ecological restoration in the Chicago Wilderness. The prototypical model provided structure and organization of the empirical data of two Chicago Wilderness member groups and revealed organizational structures, patterns of interactions via formal and informal meetings, and parameter values for the various mechanisms. The organization of the data allowed us to identify where our original model mechanisms required adaptation. After model modifications were completed, baseline scenarios were contrasted with observations for final parameter calibration and to elaborate explanations of the study cases. This exercise allowed us to identify the components and mechanisms in the system to which the outputs are most sensitive. We constructed relevant hypothetical scenarios around these critical components, and found that key liaisons, agents with high interaction frequencies and high mutual respect values are useful in promoting efficient decision processes but are limited in their ability to change the collective position with respect to a restoration practice. Simulations suggest that final collective position can be changed when there is a more equitable distribution of agents across groups, or the key liaison is very persuasive (i.e. interacts frequently and is highly respected) but is non-reciprocal (i.e. does not respect others highly). Our work advances our understanding of key mechanisms influencing collective decision processes and illustrates the value of agent-based modeling and its integration with ethnographic data analysis to advance the theory of collective decision making.

The discursive construction of decision making in interdisciplinary teams

PurposeThe purpose of this paper is to explore the analytical productiveness of a discursive power perspective in understanding interdisciplinary teams' inefficient decision processes, and to discuss the ethical consequences of such an approach.Design/methodology/approachBased on a case study of an interdisciplinary team in a Danish hospital, the paper analyzes the team's decision practices as a result of discursive power operations that privilege and marginalize groups and persons.FindingsThe paper shows that a discourse of equality dominates the team's decision practices. This produces a tendency among members to word observations as reflections whereas expert assessments are rendered unlikely and unwelcome. The paper demonstrates that this analysis is productive in understanding why interdisciplinary teams struggle to develop efficient decision processes. Furthermore, the paper suggests that managers should respond ethically to these discursive power operations with political interventions.Originality/valueA variety of theoretical models have been proposed to understand interdisciplinary teams' problematic decision processes. This paper makes an original contribution to this field by shoving how a discursive framework provides a productive analytical strategy. Moreover, the paper's proposition of political intervention as an ethical way of securing social sustainability is unprecedented.

Computational properties of argument systems satisfying graph-theoretic constraints

One difficulty that arises in abstract argument systems is that many natural questions regarding argument acceptability are, in general, computationally intractable having been classified as complete for classes such as np, co- np, and Π 2 p . In consequence, a number of researchers have considered methods for specialising the structure of such systems so as to identify classes for which efficient decision processes exist. In this paper the effect of a number of graph-theoretic restrictions is considered: k-partite systems ( k ⩾ 2 ) in which the set of arguments may be partitioned into k sets each of which is conflict-free; systems in which the numbers of attacks originating from and made upon any argument are bounded; planar systems; and, finally, those of k-bounded treewidth. For the class of bipartite graphs, it is shown that determining the acceptability status of a specific argument can be accomplished in polynomial-time under both credulous and sceptical semantics. In addition we establish the existence of polynomial time methods for systems having bounded treewidth when deciding the following: whether a given (set of) arguments is credulously accepted; if the system has a non-empty preferred extension; has a stable extension; is coherent; has at least one sceptically accepted argument. In contrast to these positive results, however, deciding whether an arbitrary set of arguments is “collectively acceptable” remains np-complete in bipartite systems. Furthermore for both planar and bounded degree systems the principal decision problems are as hard as the unrestricted cases. In deriving these latter results we introduce various concepts of “simulating” a general argument system by a restricted class so allowing any argument system to be translated to one which has both bounded degree and is planar. Finally, for the development of basic argument systems to so-called “value-based frameworks”, we present results indicating that decision problems known to be intractable in their most general form remain so even under quite severe graph-theoretic restrictions. In particular the problem of deciding “subjective acceptability” continues to be np-complete even when the underlying graph is a binary tree.

Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

Remote sensing from airborne and spaceborne platforms provides valuable data for mapping, environmental monitoring, disaster management and civil and military intelligence. However, to explore the full value of these data, the appropriate information has to be extracted and presented in standard format to import it into geo-information systems and thus allow efficient decision processes. The object-oriented approach can contribute to powerful automatic and semi-automatic analysis for most remote sensing applications. Synergetic use to pixel-based or statistical signal processing methods explores the rich information contents. Here, we explain principal strategies of object-oriented analysis, discuss how the combination with fuzzy methods allows implementing expert knowledge and describe a representative example for the proposed workflow from remote sensing imagery to GIS. The strategies are demonstrated using the first object-oriented image analysis software on the market, eCognition, which provides an appropriate link between remote sensing imagery and GIS.

An international Mars Exploration Program

A Mars Exploration Program to be executed by the international space community is being studied by the United States' National Aeronautics and Space Administration. This program is based on a set of separate missions, each conducted by different national space agencies, whose overall objective is to further the exploration of Mars by robotic means, provide precursor information for eventual human exploration of Mars, establish management relations which will carry forward to human exploration, and in the process contribute significantly to worldwide cooperation in the exploration of space and the prosperity of mankind. A mission set consisting of a high resolution imaging orbiter launched in the 1996 Mars opportunity, and a communications orbiter and rover with sample return elements launched in the 1998 opportunity, form the core of this proposed program. An optional variation would launch the sample return elements in 2001. The Martian atmospheric and surface samples would be returned to Earth approximately three years after the launch of the return elements. In addition to these core elements are penetrators, surface stations and balloons, each of which will make its own unique contribution. The responsibilities for the various elements would be allocated such that a space agency with extensive remote sensing expertise would design and implement an orbiter for the 1996 opportunity which would provide sub-meter resolution of the potential landing sites, certifying them safe from landing hazards and providing maps for planning rover traverses. In 1998 a rover would be launched to collect a variety of samples over a distance of some tens of kilometers. These samples would then be transferred to a vehicle for return to Earth. It is expected that the US and USSR would assume joint responsibility, with one nation implementing the rover and the other the sample return. Other nations or space agencies would provide the other elements which would become part of the Mars exploration infrastructure. All of the participating nations would have appropriate roles in the science investigations. A program of this complexity will inevitably present management challenges such as the establishment of mission objectives, development of the mission design, allocation of functional requirements to the flight elements, allocation of shared resources, e.g. time available on the Deep Space Network, coordination of schedules, coordination of flight and interface verification testing and coordination and conduct of mission operations, including data analysis. A key to meeting these challenges is a technical and management design that embodies efficient decision processes, simplified programmatic and technical interfaces and a non-adversarial environment.