scholarly journals To swim or not to swim: A population-level model of Xenopus tadpole decision making and locomotor behaviour

Biosystems ◽  
2017 ◽  
Vol 161 ◽  
pp. 3-14 ◽  
Author(s):  
Roman Borisyuk ◽  
Robert Merrison-Hort ◽  
Steve R. Soffe ◽  
Stella Koutsikou ◽  
Wen-Chang Li
Keyword(s):  
Decision Making ◽  
Population Level ◽  
Xenopus Tadpole ◽  
Locomotor Behaviour ◽  
Level Model

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses

2020 ◽  
Author(s):  
Monia Makhoul ◽  
Houssein H. Ayoub ◽  
Hiam Chemaitelly ◽  
Shaheen Seedat ◽  
Ghina R Mumtaz ◽  
...  
Keyword(s):  
Decision Making ◽  
Disease Progression ◽  
Vaccine Development ◽  
Severe Disease ◽  
Herd Immunity ◽  
Social Contact ◽  
Population Level ◽  
Contact Rate ◽  
Disease Case ◽  
Vaccine Impact

AbstractBackgroundSeveral SARS-CoV-2 vaccine candidates are currently in the pipeline. This study aims to inform SARS-CoV-2 vaccine development, licensure, decision-making, and implementation by determining key preferred vaccine product characteristics and associated population-level impact.MethodsVaccination impact was assessed at various efficacies using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application for China.ResultsA prophylactic vaccine with efficacy against acquisition (VES) of ≥70% is needed to eliminate this infection. A vaccine with VES <70% will still have a major impact, and may control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, or alternatively if supplemented with a moderate social-distancing intervention (<20% reduction in contact rate), or complemented with herd immunity. Vaccination is cost-effective. For a vaccine with VES of 50%, number of vaccinations needed to avert one infection is only 2.4, one severe disease case is 25.5, one critical disease case is 33.2, and one death is 65.1. Gains in effectiveness are achieved by initially prioritizing those ≥60 years. Probability of a major outbreak is virtually zero with a vaccine with VES ≥70%, regardless of number of virus introductions. Yet, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact.ConclusionsEven a partially-efficacious vaccine can offer a fundamental solution to control SARS-CoV-2 infection and at high cost-effectiveness. In addition to the primary endpoint on infection acquisition, developers should assess natural history and disease progression outcomes and/or proxy biomarkers, since such secondary endpoints may prove critical in licensure, decision-making, and vaccine impact.

scholarly journals Bridging fisheries management and biodiversity conservation norms: potential and challenges of balancing harvest in ecosystem-based frameworks

2015 ◽  
Vol 73 (6) ◽  
pp. 1659-1667 ◽  
Author(s):  
S. M. Garcia ◽  
J. Rice ◽  
A. Charles
Keyword(s):  
Decision Making ◽  
Fisheries Management ◽  
Biological Diversity ◽  
Population Level ◽  
System Level ◽  
Convention On Biological Diversity ◽  
Management Decision ◽  
Size Spectrum ◽  
Ecosystem Structure ◽  
Management Decision Making

Abstract Balanced harvesting has been proposed as a way for fisheries management to achieve the requirements of both the Law of the Sea Convention (LOSC)—to maintain stocks at the level at which they could produce MSY—and the Convention on Biological Diversity (CBD)—to maintain ecosystem structure and functioning. This paper examines these requirements and briefly presents four system-level relationships (spectra), representing ecosystem structures that might guide management decision-making aiming to meet both requirements. These spectra would fit in the widely accepted frameworks of the Ecosystem Approach enshrined in the CBD and adopted by FAO for Fisheries. A size spectrum, relating biomass to body length, is used as an example to illustrate its potential to support management decision-making—much like present stock-based harvest control rules—in more ecosystem-compliant fishing strategies at a sector or ecosystem level, as a complement to those currently used at a stock/population level.

scholarly journals The dynamics of trait variance in multi-species communities

2020 ◽  
Vol 7 (8) ◽  
pp. 200321
Author(s):  
Jan Martin Nordbotten ◽  
Folmer Bokma ◽  
Jo Skeie Hermansen ◽  
Nils Chr. Stenseth
Keyword(s):  
Differential Equations ◽  
Blow Up ◽  
Adaptive Dynamics ◽  
Population Level ◽  
Species Level ◽  
Density Distributions ◽  
Formal Stability ◽  
Speciation Event ◽  
Trait Variance ◽  
Level Model

In this paper, we establish the explicit connection between deterministic trait-based population-level models (in the form of partial differential equations) and species-level models (in the form of ordinary differential equations), in the context of eco-evolutionary systems. In particular, by starting from a population-level model of density distributions in trait space, we derive what amounts to an extension of the typical models at the species level known from adaptive dynamics literature, to account not only for abundance and mean trait values, but also explicitly for trait variances. Thus, we arrive at an explicitly polymorphic model at the species level. The derivations make precise the relationship between the parameters in the two classes of models and allow us to distinguish between notions of fitness on the population and species levels. Through a formal stability analysis, we see that exponential growth of an eigenvalue in the trait covariance matrix corresponds to a breakdown of the underlying assumptions of the species-level model. In biological terms, this may be interpreted as a speciation event: that is, we obtain an explicit notion of the blow-up of the variance of (possibly a linear combination of) traits as a precursor to speciation. Moreover, since evolutionary volatility of the mean trait value is proportional to trait variance, this provides a notion that species at the cusp of speciation are also the most adaptive. We illustrate these concepts and considerations using a numerical simulation.

scholarly journals Armed conflict, alcohol misuse, decision-making, and intimate partner violence among women in Northeastern Uganda: a population level study

2018 ◽  
Vol 12 (1) ◽  
Author(s):  
Jennifer J. Mootz ◽  
Florence Kyoheirwe Muhanguzi ◽  
Pavel Panko ◽  
Patrick Onyango Mangen ◽  
Milton L. Wainberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Intimate Partner Violence ◽  
Partner Violence ◽  
Armed Conflict ◽  
Alcohol Misuse ◽  
Population Level ◽  
Intimate Partner

scholarly journals A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2325
Author(s):  
Cong Wang ◽  
Zhongxiu Peng ◽  
Xijun Xu
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Programming Model ◽  
Programming Approach ◽  
Low Carbon ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Level Model ◽  
The Impact

To identify the impact of low-carbon policies on the location-routing problem (LRP) with cargo splitting (LRPCS), this paper first constructs the bi-level programming model of LRPCS. On this basis, the bi-level programming models of LRPCS under four low-carbon policies are constructed, respectively. The upper-level model takes the engineering construction department as the decision-maker to decide on the distribution center’s location. The lower-level model takes the logistics and distribution department as the decision-maker to make decisions on the vehicle distribution route’s scheme. Secondly, the hybrid algorithm of Ant Colony Optimization and Tabu Search (ACO-TS) is designed, and an example is introduced to verify the model’s and algorithm’s effectiveness. Finally, multiple sets of experiments are designed to explore the impact of various low-carbon policies on the decision-making of the LRPCS. The experimental results show that the influence of the carbon tax policy is the greatest, the carbon trading and carbon offset policy have a certain impact on the decision-making of the LRPCS, and the influence of the emission cap policy is the least. Based on this, we provide the relevant low-carbon policies advice and management implications.

scholarly journals A big data analysis of the relationship between future thinking and decision-making

2018 ◽  
Vol 115 (8) ◽  
pp. E1740-E1748 ◽  
Author(s):  
Robert Thorstad ◽  
Phillip Wolff
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Population Level ◽  
Future Thinking ◽  
Individual Level ◽  
The Future ◽  
The Individual ◽  
Near Future ◽  
The Relationship ◽  
Far Future

We use big data methods to investigate how decision-making might depend on future sightedness (that is, on how far into the future people’s thoughts about the future extend). In study 1, we establish a link between future thinking and decision-making at the population level in showing that US states with citizens having relatively far future sightedness, as reflected in their tweets, take fewer risks than citizens in states having relatively near future sightedness. In study 2, we analyze people’s tweets to confirm a connection between future sightedness and decision-making at the individual level in showing that people with long future sightedness are more likely to choose larger future rewards over smaller immediate rewards. In study 3, we show that risk taking decreases with increases in future sightedness as reflected in people’s tweets. The ability of future sightedness to predict decisions suggests that future sightedness is a relatively stable cognitive characteristic. This implication was supported in an analysis of tweets by over 38,000 people that showed that future sightedness has both state and trait characteristics (study 4). In study 5, we provide evidence for a potential mechanism by which future sightedness can affect decisions in showing that far future sightedness can make the future seem more connected to the present, as reflected in how people refer to the present, past, and future in their tweets over the course of several minutes. Our studies show how big data methods can be applied to naturalistic data to reveal underlying psychological properties and processes.

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