Hybridisation for versatile decision-making in game opponent AI

Hybridisation for versatile decision-making in game opponent A

Mapping wisdom as a complex adaptive system

This is the second of two papers concerning wisdom as an ecosystem appearing in sequential editions of Management & Marketing journal. The notion of wisdom as an ecosystem, or "the wisdom ecology", builds on work by Hays (2007) who first identified wisdom as an organisational construct and proposed a dynamic model of it. The centrepiece of this and its former companion paper is a relationship map of the Wisdom Ecosystem (the Causal Loop Diagram at Figure 1). The first paper, "The Ecology of Wisdom", introduced readers to the topics of wisdom and complex adaptive systems, and presented a dynamic model of the Wisdom Ecosystem. This second paper discusses systems dynamics modelling (mapping systems) and covers the Wisdom Ecosystem model in detail. It describes the four domains, or subsystems, of the Wisdom Ecosystem, Dialogue, Communal Mind, Collective Intelligence, and Wisdom, and walks readers through the model, exploring each of its 25 elements in turn. It examines the relationships amongst system elements and illuminates important aspects of systems function, providing a rare tutorial on developing and using Causal Loop Diagrams.Causal Loop Diagramming, Complexity, Dialogue, Organisational Learning, Systems Dynamics, Wisdom.

Conceptualising managerial and leadership wisdom - how many wise managers and leaders do you know?

The term ‘wisdom’ is used frequently in terms of managerial and leadership functioning and development. However, when asked ‘How many wise managers and leaders do you know?’ most people struggle to firstly name people and secondly, to explain what ‘wise or wisdom’ means because they tend to consider wisdom from a particular paradigm – philosophical, spiritual, cultural, psychological or ethical. This conceptual paper raises different perceptions of the meaning of ‘wisdom’ before broadly critiquing literature that deals with wisdom in terms of philosophical, spiritual, cultural, psychological and ethical constructs. This leads to summations that wisdom is largely contextual –someone might be considered wise in one context but not in another – and that a knowledgeable person is not necessarily a wise person. However, wisdom appears to comprise elements of knowledge, integrity and compassion. The paper then discusses wisdom as an ethical construct in management and leadership and concludes with a conceptual discussion of the vexed question ‘can wisdom be developed?

The ecology of wisdom

This is the first of two papers concerning wisdom as an ecosystem appearing in sequential editions of Management & Marketing journal. The notion of wisdom as an ecosystem, or “the wisdom ecology,” builds on work by Hays (2007) who first identified wisdom as an organisational construct and proposed a dynamic model of it. The centrepiece of this paper and the companion part to follow is a relationship map of the wisdom ecosystem (the Causal Loop Diagram at Figure 1). This first instalment provides background on wisdom and complex adaptive systems, and introduces the wisdom ecosystem model. The second instalment, “Mapping Wisdom as a Complex Adaptive System,” appearing in the next edition of Management & Marketing, explains systems dynamics modelling and discusses the wisdom ecosystem model in detail. It covers the four domains, or subsystems, of the wisdom ecosystem, Dialogue, Communal Mind, Collective Intelligence, and Wisdom, and walks readers through the model, exploring each of its 24 elements in turn. That second paper examines the relationships amongst system elements and illuminates important aspects of systems function.causal loop diagramming, complexity, dialogue, organisational learning, systems dynamics, wisdom.

Educating for Intellectual Virtue: a critique from action guidance

Virtue epistemology is among the dominant influences in mainstream epistemology today. An important commitment of one strand of virtue epistemology – responsibilist virtue epistemology (e.g., Montmarquet 1993; Zagzebski 1996; Battaly 2006; Baehr 2011) – is that it must provide regulative normative guidance for good thinking. Recently, a number of virtue epistemologists (most notably Baehr, 2013) have held that virtue epistemology not only can provide regulative normative guidance, but moreover that we should reconceive the primary epistemic aim of all education as the inculcation of the intellectual virtues. Baehr’s picture contrasts with another well-known position – that the primary aim of education is the promotion of critical thinking (Scheffler 1989; Siegel 1988; 1997; 2017). In this paper – that we hold makes a contribution to both philosophy of education and epistemology and, a fortiori, epistemology of education – we challenge this picture. We outline three criteria that any putative aim of education must meet and hold that it is the aim of critical thinking, rather than the aim of instilling intellectual virtue, that best meets these criteria. On this basis, we propose a new challenge for intellectual virtue epistemology, next to the well-known empirically-driven ‘situationist challenge’. What we call the ‘pedagogical challenge’ maintains that the intellectual virtues approach does not have available a suitably effective pedagogy to qualify the acquisition of intellectual virtue as the primary aim of education. This is because the pedagogic model of the intellectual virtues approach (borrowed largely from exemplarist thinking) is not properly action-guiding. Instead, we hold that, without much further development in virtue-based theory, logic and critical thinking must still play the primary role in the epistemology of education

Automatic goal allocation for a planetary rover with DSmT

In this chapter, we propose an approach for assigning aninterest level to the goals of a planetary rover. Assigning an interest level to goals, allows the rover to autonomously transform and reallocate the goals. The interest level is defined by data-fusing payload and navigation information. The fusion yields an 'interest map',that quantifies the level of interest of each area around the rover. In this way the planner can choose the most interesting scientific objectives to be analysed, with limited human intervention, and reallocates its goals autonomously. The Dezert-Smarandache Theory of Plausible and Paradoxical Reasoning was used for information fusion: this theory allows dealing with vague and conflicting data. In particular, it allows us to directly model the behaviour of the scientists that have to evaluate the relevance of a particular set of goals. This chaptershows an application of the proposed approach to the generation of a reliable interest map