Complexity Economics

Interactive Complexity Maps and Resilience Ratings of Countries, Corporations and Banks.

Here you may find examples of interactive Business Structure Maps, complexities and the corresponding Resilience Ratings™ of a wide variety of businesses.

In addition to the maps, which reflect the inter-dependencies within a business, you will also find the ranking of business parameters.

Click on a name and move the mouse.

EU 27 Countries (Q4 2012, Source: Eurostat)


 Bank of America  Bank of Beijing  Citibank  HSBC
 ING  JP Morgan  Wells Fargo  Morgan Stanley
 Goldman Sachs  Bank of NY Mellon


 Munich RE  Deutsche Telekom  Allianz Group  Sanju Biopharma
 Microsoft  PSA  Air France  SAP
 Abbot Lab  Accenture  Allstate Corp  Altria
 Amazon  Amgen  American Electric  Andarko
 Apache Corp  Baker Huges  Baxter Int.  Berkshire Hathaway
 Bristol Myers Squibb  Capital One  Colgate  Comcast
 Conoco Phillips  Costco Wholesale  Cvs  Devron Energy
 Dow Chemical  eBay  Eli Lilly  EMC Corp
 Emerson Electric  Exelon Corp  Fede  Ford
 Freeport Macmorran  General Dynamics  Gilead Sciences  Google
 Heinz  Honeywell  Lockheed Martin  Mastercard
 Medtronic  Metlife  Monsanto  Nat Oil Well Varco


 DJIA  BOVESPA  Tel Aviv  Moody’s Bal. Sh.
 Moody’s Income St.  S&P100  Singapore Hotels  Santiago R. Est. Off.
 Italian Healthcare  Chinese Healthcare  Employee Survey  Project (Robust)
 Project (Fragile)

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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