Mechanisms in Dynamically Complex Systems

In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. I want to draw the attention to another, radically different kind of complex system, in fact one that many scientists regard as the only genuine kind of complex system. Instead of being compositionally complex these systems rather exhibit highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical patterns in what I call “dynamically complex systems” arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics, the robustness of which calls for an explanation in terms of underlying generating mechanisms. However, I want to argue, dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms. I will explore how the notion of a mechanism has to be modified to accommodate this case. Moreover, I will show under which conditions the widespread, if not inflationary talk about mechanisms in (dynamically) complex systems stretches the notion of mechanisms beyond its reasonable limits and is no longer legitimate

Less is more: rarity trumps quality in luxury markets

The international market for luxury goods has almost doubled since 1990, with a worldwide increase of 10% annually. This trade is fuelled by a great deal of legally and illegally exploited wildlife species, putting enormous pressure on many of them, with potentially irreversible consequences. The dramatic decline of sturgeon populations exploited for their caviar, is a good example: all 27 species are threatened and the most coveted are on the verge of extinction. We aim to identify the mechanism responsible for the continued overexploitation of sturgeon species, despite caviar's ever-increasing price and the imminent loss of these species. Here, we demonstrate consumer preference for rarity over intrinsic quality: customers tasting two caviar samples more often chose the one they thought was rare, although both were identical. In a game theory model, we demonstrate that the most rational behaviour is to rush to consume rare species, even though this precipitates their extinction. We conclude that the human predisposition to place exaggerated value on rarity probably drives the entire market for luxury goods from reptile skins to exotic woods. Our findings suggest that allowing low levels of legal trade will exacerbate the arbitrary value of rare species and thereby stimulate demand. Only a total ban on trade from the wild (with very strict controls) combined with strong support for farmed equivalents will protect rare species

C-KE/I: A pragmatic framework for policy innovation

Improving policy making is key to address numerous contemporary challenges such as the environmental crisis, climate change, global inequality, financial crises, or pandemics. Policy making is a sequence of stages structuring policy problems and choices made to address them. Among these stages, policy design is a crucial phase since it impacts the quality of the policy alternatives being considered. Policy design is, however, largely neglected in the scientific literature, and in practice it is mainly conducted informally. Design theory, and more specifically Concept-Knowledge (C-K) theory, originally aimed at assisting the process of creating marketable objects, offers promises to formalize and rationalize policy design. We critically analyze this theory, showing that, despite its strengths, as it stands it is ill-adapted to support the innovative design of policy alternatives. For that purpose, we propose a framework, C-KE/I. This framework, which is inspired by and compatible with C-K, appraises innovation based on the explicit or implicit modal statements held by a certain individual or group (“E/I” stands for Explicit vs. Implicit). Through an ex-post analysis of a case study—the search for innovative policy solutions to water management problems in the Apulia Region, Italy—we illustrate the practical applicability and usefulness of our framework

On the Relation between Quantum Mechanical and Neo-Mechanistic Ontologies and Explanatory Strategies

Advocates of the New Mechanicism in philosophy of science argue that scientific explanation often consists in describing mechanisms responsible for natural phenomena. Despite its successes, one might think that this approach does not square with the ontological strictures of quantum mechanics. New Mechanists suppose that mechanisms are composed of objects with definite properties, which are interconnected via local causal interactions. Quantum mechanics calls these suppositions into question. Since mechanisms are hierarchical it appears that even macroscopic mechanisms must supervene on a set of “objects” that behave non- classically. In this paper we argue, in part by appeal to the theory of quantum decoherence, that the universal validity of quantum mechanics does not undermine neo-mechanistic ontological and explanatory claims as they occur within in classical domains. Additionally, we argue that by relaxation of certain classical assumptions, mechanistic explanatory strategies can sometimes be carried over into the quantum domain

On the rationality of decision aiding processes

International audienc

Using a sand wave model for optimal monitoring of navigation depth

In the Euro Channel to Rotterdam Harbor, sand waves reduce the navigable depth to an unacceptable level. To avoid the risk of grounding, the navigation depth is monitored and sand waves that reduce the navigation depth unacceptably are dredged. After the dredging, the sand waves slowly regain their original height. To reduce the high costs of surveying and dredging, the North Sea Service of the Department of Transport, PublicWorks andWater Management, is implementing a Decision Support System to reduce the required amount of surveys and provide optimal information on the necessity to dredge. Currently, the system predicts the growth of sand waves using a linear trend. The trend is determined from observations using a Kalman-filter including geo-statistical components to incorporate spatial dependencies. This works well for sand waves that are close to their maximum height. After dredging however, the sand wave height is far from its equilibrium and the growth rate is much higher, making the linear prediction worthless. Here we show that replacing the linear trend with a landau equation improves the predictions of the regeneration. Comparison shows that the landau equation predicts the crest evolution better than the linear equation for both undisturbed sand waves and dredged sand waves, with an root mean square error that is 25% less

Towards Bridging the Gap between Sheet Music and Audio

Sheet music and audio recordings represent and describe music on different semantic levels. Sheet music describes abstract high-level parameters such as notes, keys, measures, or repeats in a visual form. Because of its explicitness and compactness, most musicologists discuss and analyze the meaning of music on the basis of sheet music. On the contrary, most people enjoy music by listening to audio recordings, which represent music in an acoustic form. In particular, the nuances and subtleties of musical performances, which are generally not written down in the score, make the music come alive. In this paper, we address the problem of bridging the gap between the sheet music domain and the audio domain. In particular, we discuss aspects on music representations, music synchronization, and optical music recognition, while indicating various strategies and open research problems

Soft Dynamic Time Warping for Multi-Pitch Estimation and Beyond

Many tasks in music information retrieval (MIR) involve weakly aligned data, where exact temporal correspondences are unknown. The connectionist temporal classification (CTC) loss is a standard technique to learn feature representations based on weakly aligned training data. However, CTC is limited to discrete-valued target sequences and can be difficult to extend to multi-label problems. In this article, we show how soft dynamic time warping (SoftDTW), a differentiable variant of classical DTW, can be used as an alternative to CTC. Using multi-pitch estimation as an example scenario, we show that SoftDTW yields results on par with a state-of-the-art multi-label extension of CTC. In addition to being more elegant in terms of its algorithmic formulation, SoftDTW naturally extends to real-valued target sequences.Comment: Accepted at ICASSP 202