Diagrams as Vehicles for Scientific Reasoning

We argue that diagrams are not just a communicative tool but play important roles in the reasoning of biologists: in characterizing the phenomenon to be explained, identifying explanatory relations, and developing an account of the responsible mechanism. In the first two tasks diagrams facilitate applying visual processing to the detection of patterns that constitute phenomena or explanatory relations. Diagrams of a mechanism serve to guide reasoning about what parts and operations are needed and how potential parts of the mechanism are related to each other. Further they guide the development of computational models used to determine how the mechanism will behave. We illustrate each of these uses of diagrams with examples from research on circadian rhythm

Diagrams as Vehicles for Scientific Reasoning

We argue that diagrams are not just a communicative tool but play important roles in the reasoning of biologists: in characterizing the phenomenon to be explained, identifying explanatory relations, and developing an account of the responsible mechanism. In the first two tasks diagrams facilitate applying visual processing to the detection of patterns that constitute phenomena or explanatory relations. Diagrams of a mechanism serve to guide reasoning about what parts and operations are needed and how potential parts of the mechanism are related to each other. Further they guide the development of computational models used to determine how the mechanism will behave. We illustrate each of these uses of diagrams with examples from research on circadian rhythm

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.

Why do biologists use so many diagrams?

Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. Focusing on circadian rhythm research in biology to explore these roles, we examine diagrammatic formats that have been devised (a) to identify and illuminate circadian phenomena and (b) to develop and modify mechanistic explanations of these phenomena

Why do biologists use so many diagrams?

Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. Focusing on circadian rhythm research in biology to explore these roles, we examine diagrammatic formats that have been devised (a) to identify and illuminate circadian phenomena and (b) to develop and modify mechanistic explanations of these phenomena

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.

Governing in the Anthropocene: What Future Systems Thinking in Practice?

The revealing and concealing features of the metaphor ‘earth as Anthropocene’ are explored in an inquiry that asks: In the Anthropocene what possible futures emerge for systems thinking in practice? Framing choice, so important yet so poorly realised, is the starting point of the inquiry. Three extant conceptual pathway-dependencies are unpacked: governance or governing; practice or practising and ‘system’. New data on the organisational complexity within the field of cybersystemics is presented; new ‘imaginaries’ including systemic co-inquiry and institutional recovery are proposed as novel institutions and practices to facilitate systemic transformations within an Anthropocene setting. The arguments of the paper are illustrated through a research case study based on attempts to transform water and/or river situations towards systemic water governance. It is concluded that future systems research can be understood as the search for effective ‘imaginaries’ that offer fresh possibilities within an Anthropocene framing

Is statistical learning a mechanism?

Philosophers of science have offered several definitions of mechanism, most of which have biological or neuroscientific roots. In this paper, I consider whether these definitions apply equally well to cognitive science. I examine this question by looking at the case of statistical learning, which has been called a domain-general learning mechanism in the cognitive scientific literature. I argue that statistical learning does not constitute a mechanism in the philosophical sense of the term. This conclusion points to significant limitations in the scope of the mechanist philosophy when it comes to accounting for explanation in cognitive science.The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement no 284123.This is the author accepted manuscript. The final version is available from Taylor & Francis via http://dx.doi.org/10.1080/09515089.2016.116717

Spot the difference: Causal contrasts in scientific diagrams

An important function of scientific diagrams is to identify causal relationships. This commonly relies on contrasts that highlight the effects of specific difference-makers. However, causal contrast diagrams are not an obvious and easy to recognize category because they appear in many guises. In this paper, four case studies are presented to examine how causal contrast diagrams appear in a wide range of scientific reports, from experimental to observational and even purely theoretical studies. It is shown that causal contrasts can be expressed in starkly different formats, including photographs of complexly visualized macromolecules as well as line graphs, bar graphs, or plots of state spaces. Despite surface differences, however, there is a measure of conceptual unity among such diagrams. In empirical studies they generally serve not only to infer and communicate specific causal claims, but also as evidence for them. The key data of some studies is given nowhere except in the diagrams. Many diagrams show multiple causal contrasts in order to demonstrate both that an effect exists and that the effect is specific -- that is, to narrowly circumscribe the phenomenon to be explained. In a large range of scientific reports, causal contrast diagrams reflect the core epistemic claims of the researchers