Causality in concurrent systems

Concurrent systems identify systems, either software, hardware or even biological systems, that are characterized by sets of independent actions that can be executed in any order or simultaneously. Computer scientists resort to a causal terminology to describe and analyse the relations between the actions in these systems. However, a thorough discussion about the meaning of causality in such a context has not been developed yet. This paper aims to fill the gap. First, the paper analyses the notion of causation in concurrent systems and attempts to build bridges with the existing philosophical literature, highlighting similarities and divergences between them. Second, the paper analyses the use of counterfactual reasoning in ex-post analysis in concurrent systems (i.e. execution trace analysis).Comment: This is an interdisciplinary paper. It addresses a class of causal models developed in computer science from an epistemic perspective, namely in terms of philosophy of causalit

Cooperation networks and innovation: A complex system perspective to the analysis and evaluation of a EU regional innovation policy programme

Recent developments in innovation theory and policy have led policymakers to assign particular importance to supporting networks of cooperation among heterogeneous economic actors, especially in production systems composed of small and medium enterprises. Such innovative policies call for parallel innovations in policy analysis, monitoring and assessment. Our analysis of a policy experiment aimed at supporting innovation networks in the Italian region of Tuscany intends to address some issues connected with the design, monitoring and evaluation of such interventions. Combining tools from ethnographic research and social networks analysis, we explore the structural elements of the policy programme, its macroscopic impact on the regional innovation system, and the success of individual networks in attaining their specific objectives. This innovative approach allows us to derive some general methodological suggestions for the design and evaluation of similar programmes.Innovation policy, cooperation networks, evaluation, regional development, SMEs production systems, complex systems

Networked by design: can policy constraints support the development of capabilities for collaborative innovation?

While there has been some recent interest in the behavioural effects of policies in support of innovation networks, this research field is still relatively new. In particular, an important but under-researched question for policy design is “what kind of networks” should be supported, if the objective of the policy is not just to fund successful innovation projects, but also to stimulate behavioural changes in the participants, such as increasing their ability to engage in collaborative innovation. By studying the case of the innovation policy programmes implemented by the regional government of Tuscany, in Italy, between 2002 and 2008, we assess whether the imposition of constraints on the design of innovation networks has enhanced the participants’ collaborative innovation capabilities, and we draw some general implications for policy

Variational causal claims in epidemiology

The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the fact that causes be defined in terms of ‘difference-making’, this cannot fixes the causal metaphysics. Causality in epidemiology ought to be interpreted according to the epistemic theory. In this approach relations are deemed causal depending on the evidence and on the available methods. Indeed, evidence to establish causal claims requires difference-making considerations; furthermore, those definitions of cause reflect the ‘variational’ epistemology and methodology of epidemiology

Variational causal claims in epidemiology

The paper examines definitions of ‘cause’ in the epidemiological literature. Those definitions all describe causes as factors that make a difference to the distribution of disease or to individual health status. In the philosophical jargon, causes in epidemiology are difference-makers. Two claims are defended. First, it is argued that those definitions underpin an epistemology and a methodology that hinge upon the notion of variation, contra the dominant Humean paradigm according to which we infer causality from regularity. Second, despite the fact that causes be defined in terms of ‘difference-making’, this cannot fixes the causal metaphysics. Causality in epidemiology ought to be interpreted according to the epistemic theory. In this approach relations are deemed causal depending on the evidence and on the available methods. Indeed, evidence to establish causal claims requires difference-making considerations; furthermore, those definitions of cause reflect the ‘variational’ epistemology and methodology of epidemiology

Innovation, generative relationships and scaffolding structures: implications of a complexity perspective to innovation for public and private interventions

The linear model of innovation has been superseded by a variety of theoretical models that view the innovation process as systemic, complex, multi-level, multi-temporal, involving a plurality of heterogeneous economic agents. Accordingly, the emphasis of the policy discourse has changed over time. The focus has shifted from the direct public funding of basic research as an engine of innovation, to the creation of markets for knowledge goods, to, eventually, the acknowledgement that knowledge transfer very often requires direct interactions among innovating actors. In most cases, policy interventions attempt to facilitate the match between “demand” and “supply” of the knowledge needed to innovate. A complexity perspective calls for a different framing, one focused on the fostering of processes characterized by multiple agency levels, multiple temporal scales, ontological uncertainty and emergent outcomes. This contribution explores what it means to design interventions in support of innovation processes inspired by a complex systems perspective. It does so by analyzing two examples of coordinated interventions: a public policy funding innovating networks (with SMEs, research centers and university), and a private initiative, promoted by a network of medium-sized mechanical engineering firms, that supports innovation by means of technology brokerage. Relying on two unique datasets recording the interactions of the organizations involved in these interventions, social network analysis and qualitative research are combined in order to investigate network dynamics and the roles of specific actors in fostering innovation processes. Then, some general implications for the design of coordinated interventions supporting innovation in a complexity perspective are drawn

Innovative interventions in support of innovation networks. A complex system perspective to public innovation policy and private technology brokering

The linear model of innovation has been superseded by a variety of theoretical models that view the innovation process as systemic, complex, multi-level, multi-temporal, involving a plurality of heterogeneous economic agents. Accordingly, the emphasis of the policy discourse has shifted over time. It has gone from a focus on direct public funding of basic research as an engine of innovation, to the creation of markets for knowledge goods, to, eventually, the acknowledgement that knowledge transfer very often requires direct interactions among innovating actors. In most cases, these interventions attempt to facilitate the match between “demand” and “supply” of the knowledge needed to innovate. A complexity perspective calls for a different framing, one focused on the fostering of process characterized by multiple agency levels, multiple temporal scales, ontological uncertainty and emergent outcomes. The article explores what it means to design interventions in support of innovation processes inspired by a complex systems perspective. It does so by analyzing two different examples of coordinated interventions: an innovative public policy funding networks of innovating firms, and a private initiative supporting innovation in the mechanical engineering industry thanks to the set up of a technology broker. Relying on two unique datasets recording the interactions of the various organizations involved in these interventions, the article combines social network analysis and qualitative research in order to investigate the dynamics of the networks and the roles and actions of specific actors in fostering innovation processes. Building upon this comparative analysis, some general implications for the design of coordinated interventions supporting innovation in a complexity perspective are derived.Innovation policy; local development policies; regional development policies; evaluation management

Mechanisms and the Evidence Hierarchy

Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM

Reconstructing the mixed mechanisms of health: the role of bio- and socio-markers

It is widely agreed that social factors are related to health outcomes: much research served to establish correlations between classes of social factors on the one hand and classes of disease on the other hand. However, why and how social factors are an active part in the aetiology of disease development is something that is gaining attention only recently in the health sciences and in the medical humanities. In this paper, we advance the view that, just as bio-markers help trace the causal continuum from exposure to disease development at the biological level, socio-markers ought to be introduced and studied in order to trace the social continuum from exposure to disease development. We explain how socio-markers differ from social indicators and how they can be used in combination with bio-markers in order to reconstruct the mixed mechanisms of health and disease, namely mechanisms in which both biological and social factors have an active causal role