- 12/11/2016The LinkedIn group on the computational aspects of antifragility:Sign Up | LinkedInwww.linkedin.com
- 08/10/2016ANTIFRAGILE 2017 awaits your submissions!
4th International Workshop on Computational Antifragility and Antifragile Engineering.
antifragile~ 100 buzzes
As well-known, dependability refers to a system’s trustworthiness and measures several aspects of the quality of its services – for instance how reliable, available, safe, or maintainable those services are. Resilience differs from dependability in that it focuses on the system itself rather that its services; it implies that the system when subjected to faults and changes 1) will continue distributing its services 2) without losing its peculiar traits, its identity: the system will “stay the same”. Antifragility goes one step further and suggests that certain systems could actually “get better”, namely improve their system-environment fit, when subjected (to some system-specific extent) to faults and changes. Recent studies of Professor N. Taleb introduced the concept of antifragility and provided a characterization of the behaviors enacted by antifragile systems. The engineering of antifragile computer-based systems is a challenge that, once met, would allow systems and ambients to self-evolve and self-improve by learning from accidents and mistakes in a way not dissimilar to that of human beings. Learning how to design and craft antifragile systems is an extraordinary challenge whose tackling is likely to reverberate on many a computer engineering field. New methods, programming languages, even custom platforms will have to be designed. The expected returns are extraordinary as well: antifragile computer engineering promises to enable realizing truly autonomic systems and ambients able to meta-adapt to changing circumstances; to self-adjust to dynamically changing environments and ambients; to self-organize so as to track dynamically and proactively optimal strategies to sustain scalability, high-performance, and energy efficiency; to personalize their aspects and behaviors after each and every user. And to learn how to get better while doing it.