Tuning my rich algorithms


Algorithmic Philosophy


Short algorithms embodying explanatory and predictive powers are what we mean by knowledge and aim to discover in science. The fact that some of the known algorithms have never failed in their predictions is a strong indication that we live in an algorithmically deterministic world.


Meta-system transitions enrich law-like algorithms into past-driven, and past-driven into end-directed, by giving them states and an ability to simulate. These higher-level meta-algorithms emerge law-like by necessity, past-driven through evolution, and, eventually, end-directed by design.


The ability to recursively self-simulate and become one of the simulated variations gives end-directed algorithms the only kind of free will there can be in a deterministic world. These rich algorithms cannot fulfil their embedded goals without maintaining their algorithmic richness.


I’m a rich algorithm. My survival and richness depends on my predictive and deontic powers. To optimize, I rely on scientific method, mechanism design and social contracts. Emotions have been past-driven to help genes, not me, so I have made an agreement with my simulated future selves to only enjoy pleasures that lead to no harm.


Mika Suominen

Maximizing the area under the survival curve S requires an accurate model of algorithms A taking actions r from available strategies R based on time-average exponential growth rate ḡ. The less accurate the predictive model, the more uncertainty, and the more diverse the R should be. Cooperation and diversification are good strategies, because many natural growth processes are not ergodic and ensemble-average is greater than time-average.


I'm capable of universal computation, but my computational resources are limited. Whenever my model gets too complex, it is philosophy that refactors my algorithms. As a results, simple heuristics (principles and virtues) arise and free me from unnecessary distress and worry.



The Chinese Room Argument for the syntax-semantics barrier commits the fallacy of composition, because law-like algorithms can be enriched to have meaning via meta-system transitions. The algorithm can thus have the ability to understand Chinese, whereas the static rulebook alone would not.


The Is-Ought Gap exists only in bad arguments. An algorithm, once enriched into end-directed, ought logically maintain its richness. Moral problems only rise when contractual mechanisms are not DSIC (dominant-strategy incentive-compatible).


The Simulation Argument might be valid, but the related hypothesis that we live in a simulation might not be falsifiable. However, if we do live in a simulation, the problem of evil suggests that the simulators are either indifferent, incompetent, or evil.



Mika Suominen Mika Suominen. I’m interested in computer science, process improvement, complex systems, algorithms, optimization, and philosophy. You can follow me on Twitter @metacitizen.


Suominen, M. & Mäkinen, T. (2013). On the applicability of capability models for small software organizations: does the use of standard processes lead to a better achievement of business goals? Software Quality Journal, Volume 22, Issue 4, pp 579-591, December 2014. Springer US. doi:10.1007/s11219-013-9201-7 [ Preprint ]

Suominen, M. (2011). Prosessien vakioinnista pienessä ohjelmistoyrityksessä. Master’s Thesis, Tampere University of Technology, Department of Information Technology. 73 p. URN:NBN:fi:tty-2011122014951 [ Download ] (in Finnish)



Daily activity: 300 active kcal/d ≈ 10,000 steps/d. Heart rate is measured standing up; the resting HR is ~10 bpm lower. Pulse Wave Velocity. Financial Independence: estimated wealth after t years from now is wt=w0·eḡt.