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Journal articleHo M, Price H, Evans T, et al., 2025,
Enhancing foresight models with network science: measuring innovation feedbacks within the Chain-Linked Model
, Technological Forecasting and Social Change, Vol: 213, ISSN: 0040-1625A granular understanding of innovation dynamics is crucial for forecasting how and when different actors within the innovation system can make valuable contributions. Existing theoretical foundations of the foresight practice are largely qualitative and often oversimplify the innovation process. While foresight practitioners acknowledge the existence of knowledge feedback loops, these feedback loops are rarely quantified systematically in empirical forecasting studies. Innovators and funders tend to choose their dyadic relationships but rarely have visibility over the wider, dynamic innovation network. This study enriches innovation theories for the foresight practice by leveraging multilayer citation networks to explore innovation translation pathways, achieved by integrating data from market entries, clinical trials, patents, publications, funders, and grants over a 70-year period. Our analysis shows shifts in the order, prevalence, and tipping points of translation activities as technologies mature, with granularity not described in previous studies. We also examine the distinct funding patterns of major public and private entities throughout this maturation process, revealing their unique contributions and enriching sociotechnical explanations of innovation processes. This study improves the explainability of technology forecasting through innovation theories by reconstructing micro-technical innovation dynamics from first principles.
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Journal articleChan AHH, Dunning J, Beck KB, et al., 2025,
Animal social networks are robust to changing association definitions
, BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY, Vol: 79, ISSN: 0340-5443 -
Journal articleHo M, Price HCW, Evans TS, et al., 2024,
Who made the mRNA vaccine? Measuring division of labor in therapeutic innovation
, NATURE BIOTECHNOLOGY, Vol: 42, Pages: 1643-1648, ISSN: 1087-0156 -
Journal articleViegas E, Evans T, 2024,
The Behavioural House Indicator: a faster and real time small-area indicative deprivation measure for England
, Environment and Planning B: Urban Analytics and City Science, ISSN: 2399-8083Researchers have been long preoccupied with the measuring and monitoring of economic and social deprivation at small scales, neighbourhood, level in order to provide official government agencies and policy makers with more precise data insights. Whist valuable methodologies have been developed, the exercise of data collection associated with these methods tends to be expensive, time consuming, published infrequently with significant time delays, and subject to recurring changes to methodology. Here, we propose a novel method based on a straightforward methodology and data sources to generate a faster and real time indicator for deprivation at different scaling, small to larger areas. The results of our work show that our method provides a consistent view of deprivation across the regions of England and Wales, which are in line with the other indexes, but also highlight specific flash points of deep rural and highly dense urban deprivation areas that are not well captured by existing indexes. Our method is intended to aid researchers and policy makers by complementing existing but infrequent indexes.
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Journal articleShi D, Shang F, Chen B, et al., 2024,
Local dominance unveils clusters in networks
, Communications Physics, Vol: 7, ISSN: 2399-3650Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data.
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Journal articleHo M, Price HCW, Evans TS, et al., 2024,
Dynamics of technology emergence in innovation networks
, Scientific Reports, Vol: 14, ISSN: 2045-2322To create the next innovative product, participants in science need to understand which existing technologies can be combined, what new science must be discovered, and what new technologies must be invented. Knowledge of these often arrives by means of expert consensus or popularity metrics, masking key information on how intellectual efforts accumulate into technological progress. To address this shortcoming, we first present a method to establish a mathematical link between technological evolution and complex networks: a path of events that narrates innovation bottlenecks. Next, we quantify the position and proximity of documents to these innovation paths. The result is an innovation network that more exhaustively captures deterministic knowledge flows with respect to a marketed innovative product. Our dataset, containing over three million biomedical citations, demonstrates the possibility of quantifying the accumulation, speed, and division of labour in innovation over a sixty-year time horizon. The significance of this study includes the (i) use of a purpose-generated dataset showing causal paths from research to development to product; (ii) analysis of the innovation process as a directed acyclic graph; (iii) comparison between calendar time and network time; (iv) ordering of science funders along technology lifecycles; (v) quantification of innovative activities' importance to an innovative outcome; and (vi) integration of publication, patent, clinical trial, regulatory data to study innovation holistically.
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Journal articleGheorghiade P, Vasiliauskaite V, Diachenko A, et al., 2023,
Entropology: an information-theoretic approach to understanding archaeological data
, Journal of Archaeological Method and Theory, Vol: 30, Pages: 1109-1141, ISSN: 1072-5369The main objective of this paper is to develop quantitative measures for describing the diversity, homogeneity, and similarity of archaeological data. It presents new approaches to characterize the relationship between archaeological assemblages by utilizing entropy and its related attributes, primarily diversity, and by drawing inspiration from ecology. Our starting premise is that diachronic changes in our data provide a distorted reflection of social processes and that spatial differences in data indicate cultural distancing. To investigate this premise, we adopt a parsimonious approach for comparing assemblage profiles employing and comparing a range of (Hill) diversities, which enable us to exploit different aspects of the data. The modelling is tested on two seemingly large datasets: a Late Bronze Age Cretan dataset with circa 13,700 entries (compiled by PG); and a 4th millennium Western Tripolye dataset with circa 25,000 entries (compiled by AD). The contrast between the strongly geographically and culturally heterogeneous Bronze Age Crete and the strongly homogeneous Western Tripolye culture in the Southern Bug and Dnieper interfluve show the successes and limitations of our approach. Despite the seemingly large size of our datasets, these data highlight limitations that confine their utility to non-semantic analysis. This requires us to consider different ways of treating and aggregating assemblages, either as censuses or samples, contingent upon the degree of representativeness of the data. While our premise, that changes in data reflect societal changes, is supported, it is not definitively confirmed. Consequently, this paper also exemplifies the limitations of large archaeological datasets for such analyses.
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Journal articleGirn M, Rosas FE, Daws RE, et al., 2023,
A complex systems perspective on psychedelic brain action
, TRENDS IN COGNITIVE SCIENCES, Vol: 27, Pages: 433-445, ISSN: 1364-6613- Cite
- Citations: 41
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Journal articleLuppi AI, Mediano PAM, Rosas FE, et al., 2023,
Reduced emergent character of neural dynamics in patients with a disrupted connectome
, NeuroImage, Vol: 269, Pages: 1-17, ISSN: 1053-8119High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as “Integrated Information Decomposition,” which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems — including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients’ structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
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Journal articleCarhart-Harris RL, Chandaria S, Erritzoe DE, et al., 2023,
Canalization and plasticity in psychopathology
, Neuropharmacology, Vol: 226, ISSN: 0028-3908This theoretical article revives a classical bridging construct, canalization, to describe a new model of a general factor of psychopathology. To achieve this, we have distinguished between two types of plasticity, an early one that we call ‘TEMP’ for ‘Temperature or Entropy Mediated Plasticity’, and another, we call ‘canalization’, which is close to Hebbian plasticity. These two forms of plasticity can be most easily distinguished by their relationship to ‘precision’ or inverse variance; TEMP relates to increased model variance or decreased precision, whereas the opposite is true for canalization. TEMP also subsumes increased learning rate, (Ising) temperature and entropy. Dictionary definitions of ‘plasticity’ describe it as the property of being easily shaped or molded; TEMP is the better match for this. Importantly, we propose that ‘pathological’ phenotypes develop via mechanisms of canalization or increased model precision, as a defensive response to adversity and associated distress or dysphoria. Our model states that canalization entrenches in psychopathology, narrowing the phenotypic state-space as the agent develops expertise in their pathology. We suggest that TEMP – combined with gently guiding psychological support – can counter canalization. We address questions of whether and when canalization is adaptive versus maladaptive, furnish our model with references to basic and human neuroscience, and offer concrete experiments and measures to test its main hypotheses and implications.
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