Mechanisms of impulsive choice: Experiments to explore and models to map the empirical terrain

Impulsive choice is preference for a smaller-sooner (SS) outcome over a larger-later (LL) outcome when LL choices result in greater reinforcement maximization. Delay discounting is a model of impulsive choice that describes the decaying value of a reinforcer over time, with impulsive choice evident when the empirical choice-delay function is steep. Steep discounting is correlated with multiple diseases and disorders. Thus, understanding the processes underlying impulsive choice is a popular topic for investigation. Experimental research has explored the conditions that moderate impulsive choice, and quantitative models of impulsive choice have been developed that elegantly represent the underlying processes. This review spotlights experimental research in impulsive choice covering human and nonhuman animals across the domains of learning, motivation, and cognition. Contemporary models of delay discounting designed to explain the underlying mechanisms of impulsive choice are discussed. These models focus on potential candidate mechanisms, which include perception, delay and/or reinforcer sensitivity, reinforcement maximization, motivation, and cognitive systems. Although the models collectively explain multiple mechanistic phenomena, there are several cognitive processes, such as attention and working memory, that are overlooked. Future research and model development should focus on bridging the gap between quantitative models and empirical phenomena.

A responsiveness metric for the design and planning of resilient supply chains

Supply Chain Management is in constant evolution, and Supply Chain Resilience (SCR) appears as a recent offspring result of changes in how companies do business. Research efforts on the topic have led to a focus on the basic concepts of SCR, leaving a relevant research gap on the modelling and quantification of the SCR behaviour. In fact, there is not yet a consensus on SCR metrics or on how to quantify SCR. Most SCR models fail to incorporate relevant characteristics of the supply chain's performance, as are the impacts perceived by downstream customers. This work addresses such gaps, and a new resilient SC metric is proposed, which is incorporated into a developed optimisation model, where economic and responsiveness objectives are maximised when designing and planning resilient SC considering all SC entities. The model is applied to a case study that shows that decision-makers should avoid adopting universal strategies when managing their SC and instead should define the best plan for their SC operation. The impacts perceived by downstream customers are analysed. Moreover, it can be concluded that there is a correlation between the SC performance and the new SCR metric, easing the process of designing and planning the SC when resilience concerns are at stake.

Validation of a system for automatic quantitative analysis of laboratory mice behavior based on locomotor pose

Automatic recognition and accurate quantitative analysis of rodent behavior play an important role in brain neuroscience, pharmacological and toxicological. Currently, most behavior recognition systems used in experiments mainly focus on the indirect measurements of animal movement trajectories, while neglecting the changes of animal body pose that can indicate more psychological factors. Thus, this paper developed and validated an hourglass network-based behavioral quantification system (HNBQ), which uses a combination of body pose and movement parameters to quantify the activity of mice in an enclosed experimental chamber. In addition, The HNBQ was employed to record behavioral abnormalities of head scanning in the presence of food gradients in open field test (OFT). The results proved that the HNBQ in the new object recognition (NOR) experiment was highly correlated with the scores of manual observers during the latent exploration period and the cumulative exploration time. Moreover, in the OFT, HNBQ was able to capture the subtle differences in head scanning behavior of mice in the gradient experimental groups. Satisfactory results support that the combination of body pose and motor parameters can regard as a new alternative approach for quantification of animal behavior in laboratory.

Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008-2019)

This study provides a holistic and quantitative overview of over 800 mathematical methods (e.g., financial and risk models, statistical tests, statistics and advanced algorithms) taken out of sampled scientific literature on quantitative modelling, particularly, from financial and risk modelling by applying a bibliometric approach from 2008 to 2019 and a citation network analysis. This is done to elaborate on the influence in the field after the Financial Crisis 2008. We present a content analysis of journals, main topics, applied data sets and frontiers within quantitative modelling and highlight details about quantitative features such as implemented models, algorithms and aggregated model-family combinations. Moreover, we describe explications and ties to empirical stylised facts (e.g., asymmetry or nonlinearity). Finally, we discuss insights such as our main finding, namely, the non-existence of a "single-best"-approach as well as the future prospects.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43546-022-00359-3.

Connecting quantitatively derived personality-psychopathology models and neuroscience

Traditionally, personality has been conceptualized in terms of dimensions of human experience - habitual ways of thinking, feeling, and behaving. By contrast, psychopathology has traditionally been conceptualized in terms of categories of disorder - disordered thinking, feeling, and behaving. The empirical literature, however, routinely shows that psychopathology does not coalesce into readily distinguishable categories. Indeed, psychopathology tends to delineate dimensions that are relatively similar to dimensions of personality. In this special issue of Personality Neuroscience, authors took up the challenge of reconceptualizing personality and psychopathology in terms of connected and interrelated dimensions, and they considered the utility of pursuing neuroscientific inquiry from this more integrative perspective. In this editorial article, we provide the relevant background to the interface between personality, psychopathology, and neuroscience; summarize contributions to the special issue; and point toward directions for continued research and refinement. All told, it is evident that quantitatively derived, integrative models of personality-psychopathology represent a particularly promising conduit for advancing our understanding of the neurobiological foundation of human experience, both functional and dysfunctional.

Computational models of airway branching morphogenesis

The bronchial network of the mammalian lung consists of millions of dichotomous branches arranged in a highly complex, space-filling tree. Recent computational models of branching morphogenesis in the lung have helped uncover the biological mechanisms that construct this ramified architecture. In this review, we focus on three different theoretical approaches - geometric modeling, reaction-diffusion modeling, and continuum mechanical modeling - and discuss how, taken together, these models have identified the geometric principles necessary to build an efficient bronchial network, as well as the patterning mechanisms that specify airway geometry in the developing embryo. We emphasize models that are integrated with biological experiments and suggest how recent progress in computational modeling has advanced our understanding of airway branching morphogenesis.

Emerging Modeling Concepts and Solutions in Stem Cell Research

Modern stem cell research, as well as other fields of contemporary biology involves quantitative sciences in many ways. Identifying candidates for key differentiation or reprogramming factors, tracing global transcriptome changes, or finding drugs is now broadly involves bioinformatics and biostatistics. However, the next key step, understanding the underlying reasons and establishing causal links leading to differentiation or reprogramming requires qualitative and quantitative biological models describing complex biological systems. Currently, quantitative modeling is a challenging science, capable to deliver rather modest results or predictions. What model types are the most popular and what features of stem cell behavior they are capturing? What new insights do we expect from the computational modeling of stem cells in the foreseeable future? Current review attempts to approach these essential questions by considering published quantitative models and solutions emerging in the area of stem cell research.

A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models

This paper aims to contribute to developing better ways for incorporating essential human elements in decision making processes for modelling of complex socio-ecological systems. It presents a step-wise methodology for integrating perceptions of stakeholders (qualitative) into formal simulation models (quantitative) with the ultimate goal of improving understanding and communication about decision making in complex socio-ecological systems. The methodology integrates cognitive mapping and agent based modelling. It cascades through a sequence of qualitative/soft and numerical methods comprising: (1) Interviews to elicit mental models; (2) Cognitive maps to represent and analyse individual and group mental models; (3) Time-sequence diagrams to chronologically structure the decision making process; (4) All-encompassing conceptual model of decision making, and (5) computational (in this case agent-based) Model. We apply the proposed methodology (labelled ICTAM) in a case study of viticulture irrigation in South Australia. Finally, we use strengths-weakness-opportunities-threats (SWOT) analysis to reflect on the methodology. Results show that the methodology leverages the use of cognitive mapping to capture the richness of decision making and mental models, and provides a combination of divergent and convergent analysis methods leading to the construction of an Agent Based Model.