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Lecture

Randomness in Social Process

In course

CS-234: Technologies for democratic society

This course will offer students a broad but hands-on introduction to technologies of human self-organization. ...

Description

This lecture explores the concept of randomness in social processes, focusing on the random selection of authority roles like judges in Athenian and Venetian democracies. The instructor discusses the benefits of utilizing community wisdom and anti-corruption measures in the selection process.

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Instructors (2)

Verónica del Carmen Estrada Galiñanes

Bryan Alexander Ford

Official source

Related concepts (29)

Randomness

In common usage, randomness is the apparent or actual lack of definite pattern or predictability in information. A random sequence of events, symbols or steps often has no order and does not follow an intelligible pattern or combination. Individual random events are, by definition, unpredictable, but if the probability distribution is known, the frequency of different outcomes over repeated events (or "trials") is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will tend to occur twice as often as 4.

Athenian democracy

Athenian democracy developed around the 6th century BC in the Greek city-state (known as a polis) of Athens, comprising the city of Athens and the surrounding territory of Attica. Although Athens is the most famous ancient Greek democratic city-state, it was not the only one, nor was it the first; multiple other city-states adopted similar democratic constitutions before Athens. By the late 4th century BC as many as half of the over one thousand existing Greek city-states might have been democracies.

Randomness test

A randomness test (or test for randomness), in data evaluation, is a test used to analyze the distribution of a set of data to see whether it can be described as random (patternless). In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for randomness, to show that the data are valid for use in simulation runs. In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1.

Random number generation

Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain some patterns detectable in hindsight but unpredictable to foresight. True random number generators can be hardware random-number generators (HRNGs), wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model.

Statistical randomness

A numeric sequence is said to be statistically random when it contains no recognizable patterns or regularities; sequences such as the results of an ideal dice roll or the digits of π exhibit statistical randomness. Statistical randomness does not necessarily imply "true" randomness, i.e., objective unpredictability. Pseudorandomness is sufficient for many uses, such as statistics, hence the name statistical randomness. Global randomness and local randomness are different.

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