Exploring agent-based methods for the analysis of payment systems: a crisis model for StarLogo TNG

agent-based modeling, payment systems, RTGS, liquidity, crisis simulation Abstract: This paper presents an exploratory agent-based model of a real time gross settlement (RTGS) payment system. Banks are represented as agents who exchange payment requests, which are settled according to a set of simple rules. The model features the main elements of a real-life system, including a central bank acting as liquidity provider, and a simplified money market. A simulation exercise using synthetic data of BI-REL (the Italian RTGS) predicts the macroscopic impact of a disruptive event on the flow of interbank payments. The main advantage of agent - based modeling is that we can dynamically see what happens to the major variables involved. In our reduced-scale system, three hypothetical distinct phases emerge after the disruptive event: 1) a liquidity sink effect is generated and the participants’ liquidity expectations turn out to be excessive; 2) an illusory thickening of the money market follows, along with increased payment delays; and, finally 3) defaulted obligations dramatically rise. The banks cannot staunch the losses accruing on defaults, even after they become fully aware of the critical event, and a scenario emerges in which it might be necessary for the central bank to step in as liquidity provider. The methodology presented differs from traditional payment systems simulations featuring deterministic streams of payments dealt with in a centralized manner with static behavior on the part of banks. The paper is within a recent stream of empirical research that attempts to model RTGS with agent – based techniques.

Can we predict a riot? Disruptive event detection using Twitter

In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to “real world” events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale “disruptive events,” smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases

Predicting financial distress:A comparison of survival analysis and decision tree techniques

AbstractFinancial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting – edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This analysis is done over a variety of cost ratios (Type I Error cost: Type II Error cost) and prediction intervals as these differ depending on the situation. The results show that decision trees and survival analysis models have good prediction accuracy that justifies their use and supports further investigation

Dealing with discontinuity: how to sharpen up your innovation act

Report published by Advanced Institute of Management ResearchEvery now and then a disruptive event happens, such as the invention of the internet, that changes markets, industries, even societies. Successful well-managed companies thrive in mature markets by focusing on doing what they do, just a little bit better. Consequently, when a disruptive event, such as new technology or a regulatory change comes along, the successful company is often blindsided. It is just not very good at the ‘doing it different’ type of innovation. The very attributes that make it successful in stable conditions hinder its ability to detect or exploit the change. The consequences of failing to take advantage of such disruptive change are all too frequently severe. Companies lose out to new entrants. Eastman Kodak, for example, struggled to cope with a shift to digital photography. Xerox failed to capitalise on digital photocopying. Many companies missed out on the internet. Our research suggests that companies should adopt parallel routines for managing innovation related to discontinuous events, alongside their routines for managing innovation in stable conditions

Assessing and strengthening organisational resilience in a critical infrastructure system: Case study of the Slovak Republic

Critical infrastructure is a system that consists of civil infrastructures in which disruption or failure would have a serious impact on the lives and health of the population. It includes, for example, electricity, oil and gas, water supplies, communications and emergency or healthcare services. It is therefore important that technical resilience and organisational resilience is provided continuously and at a high level by the owners and operators of these civil infrastructures. Organisational resilience management mainly consists of continuously assessing determinants in order to identify weak points early so that adequate security measures can be taken to strengthen them. In the context of the above, the article presents a method for Assessing and Strengthening Organisational Resilience (ASOR Method) in a critical infrastructure system. The essence of this method lies in defining the factors that determine organisational resilience and the process of assessing and strengthening organisational resilience. The method thus allows weaknesses to be identified and the subsequent quantification of positive impacts that strengthen individual factors in organisational resilience. A benefit from applying this method is minimizing the risk and subsequent adverse impact on society of critical infrastructure system disruption or failure. The article also contributes to achieving the UN Sustainable Development Goal 9, namely Building Resilient Infrastructure. The ASOR method namely contributes to the development of quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure. Finally, the article presents the results of this method's practical application on a selected electricity critical infrastructure entity in the Slovak Republic.Web of Science123art. no. UNSP 10457

Summerview

Summerview is a thesis-length work of fiction in fulfillment of the requirements of the MFA program in Creative Writing. It is a story about a religious family with a disruptive event in its past. It is also about objects such as billboards. Everyone in the story lives in the United States of America and is afraid of something

When Should the FDA Inspect Pharmaceutical Manufacturing Facilities to Better Mitigate Drug Shortages?

Drug shortages have been a persistent problem in American healthcare for decades, and the resulting lack of access to necessary drugs has been disastrous to patient health. A majority of these shortages were caused by quality issues related to problems in the manufacturing process. More frequent inspections can reduce quality concerns, but deciding when to inspect is a complex problem; strict regulation enforcement can force low-profit facilities to close due to excessive maintenance costs, while lax enforcement allows for regulation violations to persist, both of which can cause drug shortages. We propose a novel model to assist the FDA in determining when to inspect manufacturing facilities. We formulate this problem as a finite-horizon partially observable Markov decision process (POMDP) based on the classifications the FDA assigns to each facility after inspection, as well two disruptive events: a manufacturing failure occurring or the facility closing for non-mandatory maintenance. We theoretically show that this problem can be reduced to only needing to consider whether or not to inspect immediately, which is independent of the time horizon. We additionally determine the sensitivity of the optimal inspection time on the penalty incurred for an unexpected disruptive event occurring. Our computational study demonstrates a quadratic relationship between the relative difference in average value accumulated between inspecting based on the optimal inspection time produced by our model and inspecting based on the expected time to an unexpected disruptive event, highlighting the importance of allocating more inspection resources to high-risk facilities that produce drugs that highly impact public health. We additionally find that optimal inspection time is more sensitive to changes in the penalty incurred from a disruptive event occurring the longer it has been since the last inspection