The toughest problems and challenges facing humanity originate from excessive complexity

Extreme problems are very large-scale multi-disciplinary problems, involving thousands or millions of variables and which cannot be solved using conventional technology. In such situations, it is impossible to determine the cause of the problem not only because of its sheer size but, most importantly, because it is frequently perceived through conventional eyes and distorted by narrow and linear thinking. It is not a matter of Big Data, huge compute power or sophisticated math modeling - some things just cannot be modeled. Certain problems cannot be solved with traditional mathematics. A model-free approach is mandatory.

Examples of extreme problems:
 

• Large-scale zero-day cyber attacks

• Unexpected collapses of critical systems or infrastructures (markets, transportation systems, IT networks, large corporations)

• Prolonged states of crisis, inefficiency or frequent system failures (process plants, transportation systems, economies, telephone networks)

• Sudden catastrophic collapse (spacecraft, aircraft, software systems, ecosystems, stock markets)

• Global emergencies

• Post-crisis scenario management

Clearly, extreme problems cause extreme consequences. And loss of reputation.

Extreme Problems may be solved by attacking them at their roots - their massive complexity

The solution of Extreme Problems requires the close collaboration of three parties: Ontonix, the client a supercomputer center. The approach we follow is this:

• The client and Ontonix analyze the problem together

• The consequences and losses incurred by the client are quantified

• As much data about the problem is gathered as possible

• Ontonix employs its best efforts to solve the problem

• In case of loss reduction/elimination, a percentage of the client´s gains are paid to Ontonix

But why do Extreme Problems originate in the first place? Basically because of:

• Bad design
• Evolutionary and incremental approach to new challenges
• Automation of old ways of thinking instead of innovation
• Neglecting complexity during design

Extreme Problems often lead to extreme consequences. This is because in highly complex systems:

• Malfunction may not be detectable for long periods
• Things often operate close to failure mode
• The crucial variables are discovered by accident
• The likelihood of failure increases with complexity

Solving Extreme Problems requires new approaches in terms of:

• Problem statement
• Solution strategy: data-centric not model-centric
• Large-scope, large-scale systemic approach
• World-class computational resources

Our global and inter-dependent economy is punctuated by shocks and destabilizing events which will increase in frequency and intensity. This means that we will face more extreme problems and that they will also be more severe.