Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In each scenario, individuals are faced with a forced "go" versus "no go" evacuation decision, based on information available on competing broadcast and peer-to-peer sources. In this controlled setting, all actions and observations are recorded prior to the decision, enabling development of a quantitative decision making model that accounts for the disaster likelihood, severity, and temporal urgency, as well as competition between networked individuals for limited emergency resources. Individual differences in behavior within this social setting are correlated with individual differences in inherent risk attitudes, as measured by standard psychological assessments. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios.Comment: Approved for public release; distribution is unlimite

Collective Decision Dynamics in the Presence of External Drivers

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision-making. Our results indicate that 1) social networks lead to clustering and cohesive action among individuals, 2) binary information introduces high temporal variability and stagnation, and 3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure

Aspects of microbial communities in peatland carbon cycling under changing climate and land use pressures

This is the final version. Available on open access from the Finnish Peatland Society via the DOI in this record. Globally, major efforts are being made to restore peatlands to maximise their resilience to anthropogenic climate change, which puts continuous pressure on peatland ecosystems and modifies the geography of the environmental envelope that underpins peatland functioning. A probable effect of climate change is reduction in the waterlogged conditions that are key to peatland formation and continued accumulation of carbon (C) in peat. C sequestration in peatlands arises from a delicate imbalance between primary production and decomposition, and microbial processes are potentially pivotal in regulating feedbacks between environmental change and the peatland C cycle. Increased soil temperature, caused by climate warming or disturbance of the natural vegetation cover and drainage, may result in reductions of long-term C storage via changes in microbial community composition and metabolic rates. Moreover, changes in water table depth alter the redox state and hence have broad consequences for microbial functions, including effects on fungal and bacterial communities especially methanogens and methanotrophs. This article is a perspective review of the effects of climate change and ecosystem restoration on peatland microbial communities and the implications for C sequestration and climate regulation. It is authored by peatland scientists, microbial ecologists, land managers and non-governmental organisations who were attendees at a series of three workshops held at The University of Manchester (UK) in 2019–2020. Our review suggests that the increase in methane flux sometimes observed when water tables are restored is predicated on the availability of labile carbon from vegetation and the absence of alternative terminal electron acceptors. Peatland microbial communities respond relatively rapidly to shifts in vegetation induced by climate change and subsequent changes in the quantity and quality of below-ground C substrate inputs. Other consequences of climate change that affect peatland microbial communities and C cycling include alterations in snow cover and permafrost thaw. In the face of rapid climate change, restoration of a resilient microbiome is essential to sustaining the climate regulation functions of peatland systems. Technological developments enabling faster characterisation of microbial communities and functions support progress towards this goal, which will require a strongly interdisciplinary approach.Natural Environment Research Council (NERC

Carbon Loss Pathways in Degraded Peatlands: New Insights From Radiocarbon Measurements of Peatland Waters

Peatland carbon stores are under widespread anthropogenic pressure, resulting in degradation and carbon loss. This paper presents DO14C (Dissolved Organic Carbon) dates from waters draining two eroded blanket peatland catchments in the UK. Both catchments are characterized by severe gully erosion but one additionally has extensive surface erosion on unvegetated surfaces. DO14C values ranged from 104.3 to 88.6 percent modern (present to 976 Before Present). The oldest DOC dates came from the catchment characterized by both gully and surface erosion and are among the oldest reported from waters draining temperate peatlands. Together with peat age-depth data from across the peatland landscape, the DO14C ages identify where in the peat profile carbon loss is occurring. Source depths were compared with modeled water table data indicating that in the catchment where gully erosion alone dominated, mean water table was a key control on depth of DOC production. In the system exhibiting both gully erosion and surface erosion, DOC ages were younger than expected from the age of surficial peats and measured water tables. This may indicate either that the old organic matter exposed at the surface by erosion is less labile or that there are modifications of hydrological flow pathways. Our data indicate that eroded peatlands are losing carbon from depth, and that erosion form may be a control on carbon loss. Our approach uses point measurements of DO14C to indicate DOC source depths and has the potential to act as an indicator of peatland function in degraded and restored systems

Model rate laws and their variation with shelter capacity and time pressure.

<p>In <b>A</b> we plot the measured rates for data partitioned only by (black dots with grey bars for standard deviation), along with the best fit model (Eq. 6). In <b>B</b> we plot the measured rates for the data further partitioned by shelter capacity , along with the best fit model where the mean threshold is a linear function of (). Line color indicates shelter capacity: (red; top), (orange), (green), (blue), and (black; bottom). Not all values were observed in all value scenarios. As bed number decreases, the rate curve shifts left, giving an increase in evacuation rate at the same . The model in <b>B</b> displayed systematic inaccuracies requiring partitioning the data into three different time scenarios ( before 30 seconds in 30 second or greater runs, after 30 seconds in those runs, and for 60 second runs). In <b>C</b> we plot only the 50-bed curves for the three scenarios and note that the rates for lie between and 2.</p

Mean and standard deviation (STD) for the Big Five Inventory scores calculated over all 50 participants (yellow).

<p>For comparison, we report the typical values estimated from 6076 individuals aged 21 (blue) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087380#pone.0087380-Srivastava1" target="_blank">[73]</a>. The only significant deviation from typical scores was neuroticism, which had a significantly lower mean value.</p

Overview of behavioral network science experiment.

<p><b>A: Experimental setup at UCSB. B</b>: Disaster Tab, showing current status and loss table. <b>C</b>: Social Tab, showing status of neighbors; in this example, neighbors have claimed shelter spaces 2, 5, and 18, meaning that at least 18 of 35 shelter spaces have already been filled.</p

The collective evacuation behavior in two different scenarios.

<p><b>A</b> (CertainTime): Participants wait until the end of the run to evacuate, waiting for more accurate information on the likelihood that the disaster will strike; some get stranded InTransit when the number of evacuees exceeds the shelter capacity. <b>B</b> (VariableTime): More than half the participants evacuate at approximately the 30 second mark, which is the first time that the scenario could end.</p

A comparison between data and simulation for the 47 scenarios and the best fit six-parameter model defined in Eq. 9.

<p>At each second the value (blue), the shelter capacity, and the time scenario determine the rate used in the simulation, and the expected number of evacuations is calculated. The model was fit to estimated rates (Eq. 4), not to the time series data shown here. This extends the ability of the model to predict untested scenarios. To illustrate the predictive capability we also plot the <i>leave-one-out cross-validation</i> (LOOCV) predictions (violet curves). If the model were over-fit, the LOOCV curves would have significant deviation from the full model. The reduction from 2820 rates in the data to a six-parameter model generated a model with surprising accuracy. The following runs had identical trajectories: (1,35), (3,46), (8,25), (9,36), (12,26), (13,29), (14,44,45), (15,16,38), (19,43), (22,31,33), (34,37), (39,41), (40,47).</p