Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Gina L. Tonn

dblp:201/9653 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2017
0000-0003-2633-6795ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Energy systems and smart grids · 87% Computational social science and digital humanities · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Energy systems and smart grids › power system planning and operation
power system reliability
0.312017
Hurricanes and Power System Reliability-The Effects of Individual Decisions and System-Level Hardening · Proc. IEEE 2017
Energy systems and smart grids › power system planning and operation
power system resilience
0.312017
Hurricanes and Power System Reliability-The Effects of Individual Decisions and System-Level Hardening · Proc. IEEE 2017
Computational social science and digital humanities › agent-based simulation
agent-based social simulation
0.112017
Hurricanes and Power System Reliability-The Effects of Individual Decisions and System-Level Hardening · Proc. IEEE 2017

Methods — techniques the papers use, named apart from their topics

agent-based modeling · 0.3
YearPublicationVenuePosition
2017 Hurricanes and Power System Reliability-The Effects of Individual Decisions and System-Level Hardening
abstract
Hurricanes produce significant, widespread, and often prolonged electrical-power outages. For example, Hurricane Irene caused more than 500 000 Long Island Power Authority customers to lose power and it took eight days to achieve 99% customer restoration. Individuals and businesses are heavily dependent on a continuous supply of electricity. Given this strong dependence on reliable electricity, individuals and private industries are increasingly putting collective pressure on regulators to require system hardening by utilities. In some cases, this has led to utility action. Conversely, many customers install a backup generator to guarantee electricity supply during disruptive events. These actions taken by individual customers affect their experiences in future storms, and are generally influenced by individuals' strength of preference for reliable power, their beliefs about the likelihood of losing power in the future, and the outcomes of their most recent experiences. However, individual action may come at the expense of collective action, whereby those who buy generators, often those with more resources available to purchase the generator, do not participate in the collective grievance, reducing the demand for overall system hardening. By using a validated power-outage forecasting model in conjunction with an agent-based model, we characterize how a community's likelihood of losing power in repeated hurricanes is affected by the complex interactions among individuals' behavioral responses in whether to engage in personal or collective action.
Allison C. Reilly, Gina L. Tonn, Chengwei Zhai, Seth D. Guikema
Proc. IEEE2