acm - an acm publication

2020 - October

  • COVID-19 and computation for policy

    Governments across the world are formulating and implementing medical, social, economic and other policies to manage the COVID-19 pandemic and protect their citizens. Many governments claim that their policies follow the best available scientific advice. Much of that advice comes from computational modeling. Two of the main types of model are presented: the SIR (Susceptible, Infected, Recovered) model developed by Kermack and McKendrick in the 1920s and the more recent Agent Based Models. The SIR model gives a good intuition of how epidemics spread; including how mass vaccination can contain them. It is less useful than Agent Based Modeling for investigating the effects of policies such as social distancing, self-isolation, wearing facemasks, and test-trace-isolate.

    Politicians and the public have been perplexed to observe the lack of consensus in the scientific community and there being no single 'best science' to follow. The outcome of computational models depends on the assumptions made and the data used. Different assumptions will lead to different computational outcomes, especially when the available data are so poor. This leads some commentators to argue that the models are wrong and dangerous. Some may be, but computational modeling is one of the few ways available to explore and try to understand the space of possible futures. This lack of certainty means that computational modeling must be seen as just one of many inputs into the political decision making process. Politicians must balance all the competing inputs and make timely decisions based on their conclusions---be they right or wrong. In the same way that democracy is the least worst form of government, computational modeling may be the least worst way of trying to understand the future for policy making.

  • Will post COVID-19 education be digital?: Virtual round table featuring Peter Denning, Andrew Odlyzko, Espen Andersen, and Jeffrey Johnson

    The world is experiencing large-scale social and behavioral changes in response to the COVID-19 pandemic. These changes have the potential to cause a fundamental and profound shift in the way we conduct our lives, which could have both positive and negative consequences.

    The need to function, both socially and at work, while sheltering at home and social distancing has led to the widespread realization that online meetings and remote working is viable. Digital education is particularly important, as millions of pupils and students worldwide struggle to continue studies in these difficult circumstances.

    We have posed four questions to our fellow Ubiquity editors, garnering a balanced view from academia and industry, from STEM and business (MBA) perspective, aiming to seed a follow-up debate from other editors, culminating in a free, one-hour webinar.

  • What can a bumbling, inarticulate Los Angeles cop teach us about effective communication?

    Each "Communication Corner" essay is self-contained; however, they build on each other. For best results, before reading this essay and doing the exercise, go to the first essay "How an Ugly Duckling Became a Swan," then read each succeeding essay.

    Creative (fiction) writers have an advantage over expository (non-fiction) writers. Fiction is designed to amuse and entertain, which most people look forward to. Exposition is designed to instruct and inform, which most people do not look forward to. "Columbo," the perennially popular TV series (re-runs are still being shown around the world), demonstrates how this inherent handicap can be overcome.

2018 Symposia

Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).

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Ubiquity Symposium: Big Data

Table of Contents

  1. Big Data, Digitization, and Social Change (Opening Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic
  2. Big Data and the Attention Economy by Bernardo A. Huberman
  3. Big Data for Social Science Research by Mark Birkin
  4. Technology and Business Challenges of Big Data in the Digital Economy by Dave Penkler
  5. High Performance Synthetic Information Environments: An integrating architecture in the age of pervasive data and computing By Christopher L. Barrett, Jeffery Johnson, and Madhav Marathe
  6. Developing an Open Source "Big Data" Cognitive Computing Platform by Michael Kowolenko and Mladen Vouk
  7. When Good Machine Learning Leads to Bad Cyber Security by Tegjyot Singh Sethi and Mehmed Kantardzic
  8. Corporate Security is a Big Data Problem by Louisa Saunier and Kemal Delic
  9. Big Data: Business, technology, education, and science by Jeffrey Johnson, Luca Tesei, Marco Piangerelli, Emanuela Merelli, Riccardo Paci, Nenad Stojanovic, Paulo Leitão, José Barbosa, and Marco Amador
  10. Big Data or Big Brother? That is the question now (Closing Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic