Design and development of an agent-based model for business operations faced with flood disruption
Bibliographic record
Abstract
Small and medium enterprises (SMEs) constitute a major component of the United Kingdom's economy, accounting for 99.9% of all private sector businesses and approximately 47% of annual turnover in 2014 [1].However, these companies can suffer significant financial losses as a consequence of a disruption to business operations, such as a flood event, due to their limited resources and lack of organizational plans.Agent-based modelling is recognized as one approach to enable complex problems in business and social science to be studied.SMEs' preparedness and response to disruptive events can be complex and interactive processes.Hence, agent-based modelling is an appropriate approach to study these processes and identify any emerging phenomena.With the aim of providing guidance for SMEs regarding how to better prepare and respond to the challenges faced when flooding occurs in the future, an agent-based model (ABM) is currently being designed and developed to represent and simulate SMEs' existing and potential behaviours immediately prior to, during and in the short-term aftermath of a flood event.This paper describes preliminary work on the ABM's development undertaken including the design of the various agents represented, the rules governing agent behaviours, the attributes of agents and the environment in which they operate.The basis of the ABM design draws on a range of sources including semi-structured interviews with SMEs which have experience of significant flooding, guidelines from the Environment Agency and local councils, business continuity management systems' requirements (ISO223301) and academic literature.Further, the paper discusses performance metrics of SMEs which are adopted to assess the level of continuity of business operations in the model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".