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Tuesday, January 15, 2019

Computational sociology

From Wikipedia, the free encyclopedia

Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.
 
It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate. Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence. Some of the approaches that originated in this field have been imported into the natural sciences, such as measures of network centrality from the fields of social network analysis and network science.

In relevant literature, computational sociology is often related to the study of social complexity. Social complexity concepts such as complex systems, non-linear interconnection among macro and micro process, and emergence, have entered the vocabulary of computational sociology. A practical and well-known example is the construction of a computational model in the form of an "artificial society", by which researchers can analyze the structure of a social system.

History

Historical map of research paradigms and associated scientists in sociology and complexity science.

Background

In the past four decades, computational sociology has been introduced and gaining popularity. This has been used primarily for modeling or building explanations of social processes and are depending on the emergence of complex behavior from simple activities. The idea behind emergence is that properties of any bigger system don't always have to be properties of the components that the system is made of. The people responsible for the introduction of the idea of emergence are Alexander, Morgan, and Broad, who were classical emergentists. The time at which these emergentists came up with this concept and method was during the time of the early twentieth century. The aim of this method was to find a good enough accommodation between two different and extreme ontologies, which were reductionist materialism and dualism.

While emergence has had a valuable and important role with the foundation of Computational Sociology, there are those who do not necessarily agree. One major leader in the field, Epstein, doubted the use because there were aspects that are unexplainable. Epstein put up a claim against emergentism, in which he says it "is precisely the generative sufficiency of the parts that constitutes the whole's explanation".

Agent-based models have had a historical influence on Computational Sociology. These models first came around in the 1960s, and were used to simulate control and feedback processes in organizations, cities, etc. During the 1970s, the application introduced the use of individuals as the main units for the analyses and used bottom-up strategies for modeling behaviors. The last wave occurred in the 1980s. At this time, the models were still bottom-up; the only difference is that the agents interact interdependently.

Systems theory and structural functionalism

In the post-war era, Vannevar Bush's differential analyser, John von Neumann's cellular automata, Norbert Wiener's cybernetics, and Claude Shannon's information theory became influential paradigms for modeling and understanding complexity in technical systems. In response, scientists in disciplines such as physics, biology, electronics, and economics began to articulate a general theory of systems in which all natural and physical phenomena are manifestations of interrelated elements in a system that has common patterns and properties. Following Émile Durkheim's call to analyze complex modern society sui generis, post-war structural functionalist sociologists such as Talcott Parsons seized upon these theories of systematic and hierarchical interaction among constituent components to attempt to generate grand unified sociological theories, such as the AGIL paradigm. Sociologists such as George Homans argued that sociological theories should be formalized into hierarchical structures of propositions and precise terminology from which other propositions and hypotheses could be derived and operationalized into empirical studies. Because computer algorithms and programs had been used as early as 1956 to test and validate mathematical theorems, such as the four color theorem, some scholars anticipated that similar computational approaches could "solve" and "prove" analogously formalized problems and theorems of social structures and dynamics.

Macrosimulation and microsimulation

By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission. Although simulations of social systems received substantial attention in the mid-1970s after the Club of Rome published reports predicting that policies promoting exponential economic growth would eventually bring global environmental catastrophe, the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific. Hoping to avoid the same fate, many social scientists turned their attention toward micro-simulation models to make forecasts and study policy effects by modeling aggregate changes in state of individual-level entities rather than the changes in distribution at the population level. However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.

Cellular automata and agent-based modeling

The 1970s and 1980s were also a time when physicists and mathematicians were attempting to model and analyze how simple component units, such as atoms, give rise to global properties, such as complex material properties at low temperatures, in magnetic materials, and within turbulent flows. Using cellular automata, scientists were able to specify systems consisting of a grid of cells in which each cell only occupied some finite states and changes between states were solely governed by the states of immediate neighbors. Along with advances in artificial intelligence and microcomputer power, these methods contributed to the development of "chaos theory" and "complexity theory" which, in turn, renewed interest in understanding complex physical and social systems across disciplinary boundaries. Research organizations explicitly dedicated to the interdisciplinary study of complexity were also founded in this era: the Santa Fe Institute was established in 1984 by scientists based at Los Alamos National Laboratory and the BACH group at the University of Michigan likewise started in the mid-1980s. 

This cellular automata paradigm gave rise to a third wave of social simulation emphasizing agent-based modeling. Like micro-simulations, these models emphasized bottom-up designs but adopted four key assumptions that diverged from microsimulation: autonomy, interdependency, simple rules, and adaptive behavior. Agent-based models are less concerned with predictive accuracy and instead emphasize theoretical development. In 1981, mathematician and political scientist Robert Axelrod and evolutionary biologist W.D. Hamilton published a major paper in Science titled "The Evolution of Cooperation" which used an agent-based modeling approach to demonstrate how social cooperation based upon reciprocity can be established and stabilized in a prisoner's dilemma game when agents followed simple rules of self-interest. Axelrod and Hamilton demonstrated that individual agents following a simple rule set of (1) cooperate on the first turn and (2) thereafter replicate the partner's previous action were able to develop "norms" of cooperation and sanctioning in the absence of canonical sociological constructs such as demographics, values, religion, and culture as preconditions or mediators of cooperation. Throughout the 1990s, scholars like William Sims Bainbridge, Kathleen Carley, Michael Macy, and John Skvoretz developed multi-agent-based models of generalized reciprocity, prejudice, social influence, and organizational information processing. In 1999, Nigel Gilbert published the first textbook on Social Simulation: Simulation for the social scientist and established its most relevant journal: the Journal of Artificial Societies and Social Simulation.

Data mining and social network analysis

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman. The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments. Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.

Narrative network of US Elections 2012
 
The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale, turning textual data into network data. The resulting networks, which can contain thousands of nodes, are then analyzed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes. This automates the approach introduced by quantitative narrative analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.

Computational content analysis

Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents. The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al. showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analyzing Twitter content was demonstrated as well.

The analysis of vast quantities of historical newspaper content has been pioneered by Dzogang et al., which showed how periodic structures can be automatically discovered in historical newspapers. A similar analysis was performed on social media, again revealing strongly periodic structures.

Challenges

Computational sociology, as with any field of study, faces a set of challenges. These challenges need to be handled meaningfully so as to make the maximum impact on society.

Levels and their interactions

Each society that is formed tends to be in one level or the other and there exists tendencies of interactions between and across these levels. Levels need not only be micro-level or macro-level in nature. There can be intermediate levels in which a society exists say - groups, networks, communities etc.

The question however arises as to how to identify these levels and how they come into existence? And once they are in existence how do they interact within themselves and with other levels?

If we view entities(agents) as nodes and the connections between them as the edges, we see the formation of networks. The connections in these networks do not come about based on just objective relationships between the entities, rather they are decided upon by factors chosen by the participating entities. The challenge with this process is that, it is difficult to identify when a set of entities will form a network. These networks may be of trust networks, co-operation networks, dependence networks etc. There have been cases where heterogeneous set of entities have shown to form strong and meaningful networks among themselves.

As discussed previously, societies fall into levels and in one such level, the individual level, a micro-macro link refers to the interactions which create higher-levels. There are a set of questions that needs to be answered regarding these Micro-Macro links. How they are formed? When do they converge? What is the feedback pushed to the lower levels and how are they pushed?

Another major challenge in this category concerns the validity of information and their sources. In recent years there has been a boom in information gathering and processing. However, little attention was paid to the spread of false information between the societies. Tracing back the sources and finding ownership of such information is difficult.

Culture modeling

The evolution of the networks and levels in the society brings about cultural diversity. A thought which arises however is that, when people tend to interact and become more accepting of other cultures and beliefs, how is it that diversity still persists? Why is there no convergence? A major challenge is how to model these diversities. Are there external factors like mass media, locality of societies etc. which influence the evolution or persistence of cultural diversities?

Experimentation and evaluation

Any study or modelling when combined with experimentation needs to be able to address the questions being asked. Computational social science deals with large scale data and the challenge becomes much more evident as the scale grows. How would one design informative simulations on a large scale? And even if a large scale simulation is brought up, how is the evaluation supposed to be performed?

Model choice and model complexities

Another challenge is identifying the models that would best fit the data and the complexities of these models. These models would help us predict how societies might evolve over time and provide possible explanations on how things work.

Generative models

Generative models helps us to perform extensive qualitative analysis in a controlled fashion. A model proposed by Epstein, is the agent-based simulation, which talks about identifying an initial set of heterogeneous entities(agents) and observe their evolution and growth based on simple local rules.

But what are these local rules? How does one identify them for a set of heterogeneous agents? Evaluation and impact of these rules state a whole new set of difficulties.

Heterogeneous or ensemble models

Integrating simple models which perform better on individual tasks to form a Hybrid model is an approach that can be looked into. These models can offer better performance and understanding of the data. However the trade-off of identifying and having a deep understanding of the interactions between these simple models arises when one needs to come up with one combined, well performing model. Also, coming up with tools and applications to help analyze and visualize the data based on these hybrid models is another added challenge.

Impact

Computational sociology can bring impacts to science, technology and society.

Impact on science

In order for the study of computational sociology to be effective, there has to be valuable innovations. These innovation can be of the form of new data analytics tools, better models and algorithms. The advent of such innovation will be a boon for the scientific community in large.

Impact on society

One of the major challenges of computational sociology is the modelling of social processes. Various law and policy makers would be able to see efficient and effective paths to issue new guidelines and the mass in general would be able to evaluate and gain fair understanding of the options presented in front of them enabling an open and well balanced decision process..

Journals and academic publications

The most relevant journal of the discipline is the Journal of Artificial Societies and Social Simulation.

Associations, conferences and workshops

Academic programs, departments and degrees

Centers and institutes

USA

Europe

Asia

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Computer music

From Wikipedia, the free encyclopedia

Computer music is the application of computing technology in music composition, to help human composers create new music or to have computers independently create music, such as with algorithmic composition programs. It includes the theory and application of new and existing computer software technologies and basic aspects of music, such as sound synthesis, digital signal processing, sound design, sonic diffusion, acoustics, and psychoacoustics. The field of computer music can trace its roots back to the origins of electronic music, and the very first experiments and innovations with electronic instruments at the turn of the 20th century.

In the 2000s, with the widespread availability of relatively affordable home computers that have a fast processing speed, and the growth of home recording using digital audio recording systems ranging from Garageband to Protools, the term is sometimes used to describe music that has been created using digital technology.

History

CSIRAC, Australia's first digital computer, as displayed at the Melbourne Museum
 
Much of the work on computer music has drawn on the relationship between music and mathematics, a relationship which has been noted since the Ancient Greeks described the "harmony of the spheres".
Musical melodies were first generated by the computer originally named the CSIR Mark 1 (later renamed CSIRAC) in Australia in 1950. There were newspaper reports from America and England (early and recently) that computers may have played music earlier, but thorough research has debunked these stories as there is no evidence to support the newspaper reports (some of which were obviously speculative). Research has shown that people speculated about computers playing music, possibly because computers would make noises, but there is no evidence that they actually did it.

The world's first computer to play music was the CSIR Mark 1 (later named CSIRAC), which was designed and built by Trevor Pearcey and Maston Beard from the late 1940s. Mathematician Geoff Hill programmed the CSIR Mark 1 to play popular musical melodies from the very early 1950s. In 1950 the CSIR Mark 1 was used to play music, the first known use of a digital computer for the purpose. The music was never recorded, but it has been accurately reconstructed. In 1951 it publicly played the "Colonel Bogey March" of which only the reconstruction exists. However, the CSIR Mark 1 played standard repertoire and was not used to extend musical thinking or composition practice, as Max Mathews did, which is current computer-music practice. 

The first music to be performed in England was a performance of the British National Anthem that was programmed by Christopher Strachey on the Ferranti Mark I, late in 1951. Later that year, short extracts of three pieces were recorded there by a BBC outside broadcasting unit: the National Anthem, "Ba, Ba Black Sheep, and "In the Mood" and this is recognised as the earliest recording of a computer to play music. This recording can be heard at the this Manchester University site. Researchers at the University of Canterbury, Christchurch declicked and restored this recording in 2016 and the results may be heard on Soundcloud.

Two further major 1950s developments were the origins of digital sound synthesis by computer, and of algorithmic composition programs beyond rote playback. Max Mathews at Bell Laboratories developed the influential MUSIC I program and its descendants, further popularizing computer music through a 1963 article in Science. Among other pioneers, the musical chemists Lejaren Hiller and Leonard Isaacson worked on a series of algorithmic composition experiments from 1956-9, manifested in the 1957 premiere of the Illiac Suite for string quartet.

In Japan, experiments in computer music date back to 1962, when Keio University professor Sekine and Toshiba engineer Hayashi experimented with the TOSBAC computer. This resulted in a piece entitled TOSBAC Suite, influenced by the Illiac Suite. Later Japanese computer music compositions include a piece by Kenjiro Ezaki presented during Osaka Expo '70 and "Panoramic Sonore" (1974) by music critic Akimichi Takeda. Ezaki also published an article called "Contemporary Music and Computers" in 1970. Since then, Japanese research in computer music has largely been carried out for commercial purposes in popular music, though some of the more serious Japanese musicians used large computer systems such as the Fairlight in the 1970s.

The programming computer for Yamaha's first FM synthesizer GS1. CCRMA, Stanford University
 
Early computer-music programs typically did not run in real time. Programs would run for hours or days, on multimillion-dollar computers, to generate a few minutes of music. One way around this was to use a 'hybrid system', most notably the Roland MC-8 Microcomposer, where a microprocessor-based system controls an analog synthesizer, released in 1978. John Chowning's work on FM synthesis from the 1960s to the 1970s allowed much more efficient digital synthesis, eventually leading to the development of the affordable FM synthesis-based Yamaha DX7 digital synthesizer, released in 1983. In addition to the Yamaha DX7, the advent of inexpensive digital chips and microcomputers opened the door to real-time generation of computer music. In the 1980s, Japanese personal computers such as the NEC PC-88 came installed with FM synthesis sound chips and featured audio programming languages such as Music Macro Language (MML) and MIDI interfaces, which were most often used to produce video game music, or chiptunes. By the early 1990s, the performance of microprocessor-based computers reached the point that real-time generation of computer music using more general programs and algorithms became possible.
Interesting sounds must have a fluidity and changeability that allows them to remain fresh to the ear. In computer music this subtle ingredient is bought at a high computational cost, both in terms of the number of items requiring detail in a score and in the amount of interpretive work the instruments must produce to realize this detail in sound.

Advances

Advances in computing power and software for manipulation of digital media have dramatically affected the way computer music is generated and performed. Current-generation micro-computers are powerful enough to perform very sophisticated audio synthesis using a wide variety of algorithms and approaches. Computer music systems and approaches are now ubiquitous, and so firmly embedded in the process of creating music that we hardly give them a second thought: computer-based synthesizers, digital mixers, and effects units have become so commonplace that use of digital rather than analog technology to create and record music is the norm, rather than the exception.

Research

Despite the ubiquity of computer music in contemporary culture, there is considerable activity in the field of computer music, as researchers continue to pursue new and interesting computer-based synthesis, composition, and performance approaches. Throughout the world there are many organizations and institutions dedicated to the area of computer and electronic music study and research, including the ICMA (International Computer Music Association), C4DM (Center for Digital Music), IRCAM, GRAME, SEAMUS (Society for Electro Acoustic Music in the United States), CEC (Canadian Electroacoustic Community), and a great number of institutions of higher learning around the world.

Computer-generated music

Computer-generated music is music composed by, or with the extensive aid of, a computer. Although any music which uses computers in its composition or realisation is computer-generated to some extent, the use of computers is now so widespread (in the editing of pop songs, for instance) that the phrase computer-generated music is generally used to mean a kind of music which could not have been created without the use of computers.

We can distinguish two groups of computer-generated music: music in which a computer generated the score, which could be performed by humans, and music which is both composed and performed by computers. There is a large genre of music that is organized, synthesized, and created on computers.

Music composed and performed by computers

Later, composers such as Gottfried Michael Koenig had computers generate the sounds of the composition as well as the score. Koenig produced algorithmic composition programs which were a generalisation of his own serial composition practice. This is not exactly similar to Xenakis' work as he used mathematical abstractions and examined how far he could explore these musically. Koenig's software translated the calculation of mathematical equations into codes which represented musical notation. This could be converted into musical notation by hand and then performed by human players. His programs Project 1 and Project 2 are examples of this kind of software. Later, he extended the same kind of principles into the realm of synthesis, enabling the computer to produce the sound directly. SSP is an example of a program which performs this kind of function. All of these programs were produced by Koenig at the Institute of Sonology in Utrecht in the 1970s.

Procedures such as those used by Koenig and Xenakis are still in use today. Since the invention of the MIDI system in the early 1980s, for example, some people have worked on programs which map MIDI notes to an algorithm and then can either output sounds or music through the computer's sound card or write an audio file for other programs to play.

Some of these simple programs are based on fractal geometry, and can map midi notes to specific fractals, or fractal equations. Although such programs are widely available and are sometimes seen as clever toys for the non-musician, some professional musicians have given them attention also. The resulting 'music' can be more like noise, or can sound quite familiar and pleasant. As with much algorithmic music, and algorithmic art in general, more depends on the way in which the parameters are mapped to aspects of these equations than on the equations themselves. Thus, for example, the same equation can be made to produce both a lyrical and melodic piece of music in the style of the mid-nineteenth century, and a fantastically dissonant cacophony more reminiscent of the avant-garde music of the 1950s and 1960s.

Other programs can map mathematical formulae and constants to produce sequences of notes. In this manner, an irrational number can give an infinite sequence of notes where each note is a digit in the decimal expression of that number. This sequence can in turn be a composition in itself, or simply the basis for further elaboration.

Operations such as these, and even more elaborate operations can also be performed in computer music programming languages such as Max/MSP, Reaktor, SuperCollider, Csound, Pure Data (Pd), Keykit, and ChucK. These programs now easily run on most personal computers, and are often capable of more complex functions than those which would have necessitated the most powerful mainframe computers several decades ago.

There exist programs that generate "human-sounding" melodies by using a vast database of phrases. One example is Band-in-a-Box, which is capable of creating jazz, blues and rock instrumental solos with almost no user interaction. Another is Impro-Visor, which uses a stochastic context-free grammar to generate phrases and complete solos.

Another 'cybernetic' approach to computer composition uses specialized hardware to detect external stimuli which are then mapped by the computer to realize the performance. Examples of this style of computer music can be found in the middle-80's work of David Rokeby (Very Nervous System) where audience/performer motions are 'translated' to MIDI segments. Computer controlled music is also found in the performance pieces by the Canadian composer Udo Kasemets such as the Marce(ntennia)l Circus C(ag)elebrating Duchamp (1987), a realization of the Marcel Duchamp process piece Erratum Musical using an electric model train to collect a hopper-car of stones to be deposited on a drum wired to an Analog:Digital converter, mapping the stone impacts to a score display (performed in Toronto by pianist Gordon Monahan during the 1987 Duchamp Centennial), or his installations and performance works (e.g. Spectrascapes) based on his Geo(sono)scope (1986) 15x4-channel computer-controlled audio mixer. In these latter works, the computer generates sound-scapes from tape-loop sound samples, live shortwave or sine-wave generators.

Computer-generated scores for performance by human players

Many systems for generating musical scores actually existed well before the time of computers. One of these was Musikalisches Würfelspiel (Musical dice game; 18th century), a system which used throws of the dice to randomly select measures from a large collection of small phrases. When patched together, these phrases combined to create musical pieces which could be performed by human players. Although these works were not actually composed with a computer in the modern sense, it uses a rudimentary form of the random combinatorial techniques sometimes used in computer-generated composition.

The world's first digital computer music was generated in Australia by programmer Geoff Hill on the CSIRAC computer which was designed and built by Trevor Pearcey and Maston Beard, although it was only used to play standard tunes of the day. Subsequently, one of the first composers to write music with a computer was Iannis Xenakis. He wrote programs in the FORTRAN language that generated numeric data that he transcribed into scores to be played by traditional musical instruments. An example is ST/48 of 1962. Although Xenakis could well have composed this music by hand, the intensity of the calculations needed to transform probabilistic mathematics into musical notation was best left to the number-crunching power of the computer.

Computers have also been used in an attempt to imitate the music of great composers of the past, such as Mozart. A present exponent of this technique is David Cope. He wrote computer programs that analyse works of other composers to produce new works in a similar style. He has used this program to great effect with composers such as Bach and Mozart (his program Experiments in Musical Intelligence is famous for creating "Mozart's 42nd Symphony"), and also within his own pieces, combining his own creations with that of the computer.

Melomics, a research project from the University of Málaga, Spain, developed a computer composition cluster named Iamus, which composes complex, multi-instrument pieces for editing and performance. Since its inception, Iamus has composed a full album in 2012, appropriately named Iamus, which New Scientist described as "The first major work composed by a computer and performed by a full orchestra." The group has also developed an API for developers to utilize the technology, and makes its music available on its website.

Computer-aided algorithmic composition

Diagram illustrating the position of CAAC in relation to other Generative music Systems
 
Computer-aided algorithmic composition (CAAC, pronounced "sea-ack") is the implementation and use of algorithmic composition techniques in software. This label is derived from the combination of two labels, each too vague for continued use. The label computer-aided composition lacks the specificity of using generative algorithms. Music produced with notation or sequencing software could easily be considered computer-aided composition. The label algorithmic composition is likewise too broad, particularly in that it does not specify the use of a computer. The term computer-aided, rather than computer-assisted, is used in the same manner as computer-aided design.

Machine improvisation

Machine improvisation uses computer algorithms to create improvisation on existing music materials. This is usually done by sophisticated recombination of musical phrases extracted from existing music, either live or pre-recorded. In order to achieve credible improvisation in particular style, machine improvisation uses machine learning and pattern matching algorithms to analyze existing musical examples. The resulting patterns are then used to create new variations "in the style" of the original music, developing a notion of stylistic reinjection. This is different from other improvisation methods with computers that use algorithmic composition to generate new music without performing analysis of existing music examples .

Statistical style modeling

Style modeling implies building a computational representation of the musical surface that captures important stylistic features from data. Statistical approaches are used to capture the redundancies in terms of pattern dictionaries or repetitions, which are later recombined to generate new musical data. Style mixing can be realized by analysis of a database containing multiple musical examples in different styles. Machine Improvisation builds upon a long musical tradition of statistical modeling that began with Hiller and Isaacson's Illiac Suite for String Quartet (1957) and Xenakis' uses of Markov chains and stochastic processes. Modern methods include the use of lossless data compression for incremental parsing, prediction suffix tree and string searching by factor oracle algorithm (basically a factor oracle is a finite state automaton constructed in linear time and space in an incremental fashion).

Uses of machine improvisation

Machine improvisation encourages musical creativity by providing automatic modeling and transformation structures for existing music. This creates a natural interface with the musician without need for coding musical algorithms. In live performance, the system re-injects the musician's material in several different ways, allowing a semantics-level representation of the session and a smart recombination and transformation of this material in real-time. In offline version, machine improvisation can be used to achieve style mixing, an approach inspired by Vannevar Bush's memex imaginary machine.

Implementations

The first system implementing interactive machine improvisation by means of Markov models and style modeling techniques is the Continuator, developed by François Pachet at Sony CSL Paris in 2002 based on work on non-real time style modeling. Matlab implementation of the Factor Oracle machine improvisation can be found as part of Computer Audition toolbox. There is also an NTCC implementation of the Factor Oracle machine improvisation.

OMax is a software environment developed in IRCAM. OMax uses OpenMusic and Max. It is based on researches on stylistic modeling carried out by Gerard Assayag and Shlomo Dubnov and on researches on improvisation with the computer by G. Assayag, M. Chemillier and G. Bloch (a.k.a. the OMax Brothers) in the Ircam Music Representations group.

Musicians working with machine improvisation

Gerard Assayag (IRCAM, France), Jeremy Baguyos (University of Nebraska at Omaha, US) Tim Blackwell (Goldsmiths College, Great Britain), George Bloch (Composer, France), Marc Chemiller (IRCAM/CNRS, France), Nick Collins (University of Sussex, UK), Shlomo Dubnov (Composer, Israel / US), Mari Kimura (Juilliard, New York City), Amanuel Zarzowski (Composer Los Angeles/San Diego), George Lewis (Columbia University, New York City), Bernard Lubat (Pianist, France), François Pachet (Sony CSL, France), Joel Ryan (Institute of Sonology, Netherlands), Michel Waisvisz (STEIM, Netherlands), David Wessel (CNMAT, California), Michael Young (Goldsmiths College, Great Britain), Pietro Grossi (CNUCE, Institute of the National Research Council, Pisa, Italy), Toby Gifford and Andrew Brown (Griffith University, Brisbane, Australia), Davis Salks (jazz composer, Hamburg, PA, US), Doug Van Nort (electroacoustic improviser, Montreal/New York).

Live coding

Live coding (sometimes known as 'interactive programming', 'on-the-fly programming', 'just in time programming') is the name given to the process of writing software in realtime as part of a performance. Recently it has been explored as a more rigorous alternative to laptop musicians who, live coders often feel, lack the charisma and pizzazz of musicians performing live.

Generally, this practice stages a more general approach: one of interactive programming, of writing (parts of) programs while they are interpreted. Traditionally most computer music programs have tended toward the old write/compile/run model which evolved when computers were much less powerful. This approach has locked out code-level innovation by people whose programming skills are more modest. Some programs have gradually integrated real-time controllers and gesturing (for example, MIDI-driven software synthesis and parameter control). Until recently, however, the musician/composer rarely had the capability of real-time modification of program code itself. This legacy distinction is somewhat erased by languages such as ChucK, SuperCollider, and Impromptu.

TOPLAP, an ad-hoc conglomerate of artists interested in live coding was formed in 2004, and promotes the use, proliferation and exploration of a range of software, languages and techniques to implement live coding. This is a parallel and collaborative effort e.g. with research at the Princeton Sound Lab, the University of Cologne, and the Computational Arts Research Group at Queensland University of Technology.
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Generative art

From Wikipedia, the free encyclopedia

Condensation Cube, Plexiglas and water; Hirshhorn Museum and Sculpture Garden, begun 1965, completed 2008 by Hans Haacke
 
Iridem for trombone and clarinet, 1983 by Sergio Maltagliati
 
Interactive installation 'CIMs series, 2000 by Maurizio Bolognini
 
Installation view of Irrational Geometrics 2008 by Pascal Dombis
 
Telepresence-based installation 10.000 Moving Cities, 2016 by Marc Lee
 
Generative art refers to art that in whole or in part has been created with the use of an autonomous system. An autonomous system in this context is generally one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist. In some cases the human creator may claim that the generative system represents their own artistic idea, and in others that the system takes on the role of the creator.

"Generative art" often refers to algorithmic art (algorithmically determined computer generated artwork), but artists can also make it using systems of chemistry, biology, mechanics and robotics, smart materials, manual randomization, mathematics, data mapping, symmetry, tiling, and more.

History

The use of the word "generative" in the discussion of art has developed over time. The use of "Artificial DNA" defines a generative approach to art focused on the construction of a system able to generate unpredictable events, all with a recognizable common character. The use of autonomous systems, required by some contemporary definitions, focuses a generative approach where the controls are strongly reduced. This approach is also named "emergent". Margaret Boden and Ernest Edmonds have noted the use of the term "generative art" in the broad context of automated computer graphics in the 1960s, beginning with artwork exhibited by Georg Nees and Frieder Nake in 1965:
The terms "generative art" and "computer art" have been used in tandem, and more or less interchangeably, since the very earliest days.
The first such exhibition showed the work of Nees in February 1965, which some claim was titled "Generative Computergrafik". While Nees does not himself remember, this was the title of his doctoral thesis published a few years later. The correct title of the first exhibition and catalog was "computer-grafik". "Generative art" and related terms was in common use by several other early computer artists around this time, including Manfred Mohr. The term "Generative Art" with the meaning of dynamic artwork-systems able to generate multiple artwork-events was clearly used the first time for the "Generative Art" conference in Milan in 1998.

The term has also been used to describe geometric abstract art where simple elements are repeated, transformed, or varied to generate more complex forms. Thus defined, generative art was practised by the Argentinian artists Eduardo McEntyre and Miguel Ángel Vidal in the late 1960s. In 1972 the Romanian-born Paul Neagu created the Generative Art Group in Britain. It was populated exclusively by Neagu using aliases such as "Hunsy Belmood" and "Edward Larsocchi." In 1972 Neagu gave a lecture titled 'Generative Art Forms' at the Queen's University, Belfast Festival.

In 1970 the School of the Art Institute of Chicago created a department called "Generative Systems." As described by Sonia Landy Sheridan the focus was on art practices using the then new technologies for the capture, inter-machine transfer, printing and transmission of images, as well as the exploration of the aspect of time in the transformation of image information.

In 1988 Clauser  identified the aspect of systemic autonomy as a critical element in generative art:
It should be evident from the above description of the evolution of generative art that process (or structuring) and change (or transformation) are among its most definitive features, and that these features and the very term 'generative' imply dynamic development and motion. ...

(the result) is not a creation by the artist but rather the product of the generative process - a self-precipitating structure.
In 1989 Celestino Soddu defined the Generative Design approach to Architecture and Town Design in his book Citta' Aleatorie.

In 1989 Franke referred to "generative mathematics" as "the study of mathematical operations suitable for generating artistic images."

From the mid-1990s Brian Eno popularized the terms generative music and generative systems, making a connection with earlier experimental music by Terry Riley, Steve Reich and Philip Glass.

From the end of the 20th century, communities of generative artists, designers, musicians and theoreticians began to meet, forming cross-disciplinary perspectives. The first meeting about generative Art was in 1998, at the inaugural International Generative Art conference at Politecnico di Milano University, Italy. In Australia, the Iterate conference on generative systems in the electronic arts followed in 1999. On-line discussion has centered around the eu-gene mailing list, which began late 1999, and has hosted much of the debate which has defined the field. These activities have more recently been joined by the Generator.x conference in Berlin starting in 2005. In 2012 the new journal GASATHJ, Generative Art Science and Technology Hard Journal was founded by Celestino Soddu and Enrica Colabella  jointing several generative artists and scientists in the Editorial Board.

Some have argued that as a result of this engagement across disciplinary boundaries, the community has converged on a shared meaning of the term. As Boden and Edmonds put it in 2011:
Today, the term "Generative Art" is still current within the relevant artistic community. Since 1998 a series of conferences have been held in Milan with that title (Generativeart.com), and Brian Eno has been influential in promoting and using generative art methods (Eno, 1996). Both in music and in visual art, the use of the term has now converged on work that has been produced by the activation of a set of rules and where the artist lets a computer system take over at least some of the decision-making (although, of course, the artist determines the rules).
In the call of the Generative Art conferences in Milan (annually starting from 1998), the definition of Generative Art by Celestino Soddu:
Generative Art is the idea realized as genetic code of artificial events, as construction of dynamic complex systems able to generate endless variations. Each Generative Project is a concept-software that works producing unique and non-repeatable events, like music or 3D Objects, as possible and manifold expressions of the generating idea strongly recognizable as a vision belonging to an artist / designer / musician / architect /mathematician.
Discussion on the eu-gene mailing list was framed by the following definition by Adrian Ward from 1999:
Generative art is a term given to work which stems from concentrating on the processes involved in producing an artwork, usually (although not strictly) automated by the use of a machine or computer, or by using mathematic or pragmatic instructions to define the rules by which such artworks are executed.
A similar definition is provided by Philip Galanter:
Generative art refers to any art practice where the artist creates a process, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is then set into motion with some degree of autonomy contributing to or resulting in a completed work of art.

Types

Music

Johann Philipp Kirnberger's "Musikalisches Würfelspiel" (Musical Dice Game) 1757 is considered an early example of a generative system based on randomness. Dice were used to select musical sequences from a numbered pool of previously composed phrases. This system provided a balance of order and disorder. The structure was based on an element of order on one hand, and disorder on the other.

The fugues of J.S. Bach could be considered generative, in that there is a strict underlying process that is followed by the composer. Similarly, serialism follows strict procedures which, in some cases, can be set up to generate entire compositions with limited human intervention.

Composers such as John Cage, Farmers Manual and Brian Eno have used generative systems in their works.

Visual art

The artist Ellsworth Kelly created paintings by using chance operations to assign colors in a grid. He also created works on paper that he then cut into strips or squares and reassembled using chance operations to determine placement.

Album de 10 sérigraphies sur 10 ans, by François Morellet, 2009
 
Iapetus, by Jean-Max Albert, 1985
 
Calmoduline Monument, by Jean-Max Albert, 1991
 
Artists such as Hans Haacke have explored processes of physical and social systems in artistic context. François Morellet has used both highly ordered and highly disordered systems in his artwork. Some of his paintings feature regular systems of radial or parallel lines to create Moiré Patterns. In other works he has used chance operations to determine the coloration of grids. Sol LeWitt created generative art in the form of systems expressed in natural language and systems of geometric permutation. Harold Cohen's AARON system is a longstanding project combining software artificial intelligence with robotic painting devices to create physical artifacts. Steina and Woody Vasulka are video art pioneers who used analog video feedback to create generative art. Video feedback is now cited as an example of deterministic chaos, and the early explorations by the Vasulkas anticipated contemporary science by many years. Software systems exploiting evolutionary computing to create visual form include those created by Scott Draves and Karl Sims. The digital artist Joseph Nechvatal has exploited models of viral contagion. Autopoiesis by Ken Rinaldo includes fifteen musical and robotic sculptures that interact with the public and modify their behaviors based on both the presence of the participants and each other. Jean-Pierre Hebert and Roman Verostko are founding members of the Algorists, a group of artists who create their own algorithms to create art. A. Michael Noll, of Bell Telephone Laboratories, Incorporated, programmed computer art using mathematical equations and programmed randomness, starting in 1962. The French artist Jean-Max Albert, beside environmental sculptures like Iapetus, and O=C=O, developed a project dedicated to the vegetation itself, in terms of biological activity. The Calmoduline Monument project is based on the property of a protein, calmodulin, to bond selectively to calcium. Exterior physical constraints (wind, rain, etc.) modify the electric potential of the cellular membranes of a plant and consequently the flux of calcium. However, the calcium controls the expression of the calmoduline gene. The plant can thus, when there is a stimulus, modify its « typical » growth pattern. So the basic principle of this monumental sculpture is that to the extent that they could be picked up and transported, these signals could be enlarged, translated into colors and shapes, and show the plant’s « decisions » suggesting a level of fundamental biological activity.

Maurizio Bolognini works with generative machines to address conceptual and social concerns. Mark Napier is a pioneer in data mapping, creating works based on the streams of zeros and ones in ethernet traffic, as part of the "Carnivore" project. Martin Wattenberg pushed this theme further, transforming "data sets" as diverse as musical scores (in "Shape of Song", 2001) and Wikipedia edits (History Flow, 2003, with Fernanda Viegas) into dramatic visual compositions. The Canadian artist San Base developed a "Dynamic Painting" algorithm in 2002. Using computer algorithms as "brush strokes," Base creates sophisticated imagery that evolves over time to produce a fluid, never-repeating artwork.

Software art

For some artists, graphic user interfaces and computer code have become an independent art form in themselves. Adrian Ward created Auto-Illustrator as a commentary on software and generative methods applied to art and design.

Architecture

In 1987 Celestino Soddu created the artificial DNA of Italian Medieval towns able to generate endless 3D models of cities identifiable as belonging to the idea.

Literature

Writers such as Tristan Tzara, Brion Gysin, and William Burroughs used the cut-up technique to introduce randomization to literature as a generative system. Jackson Mac Low produced computer-assisted poetry and used algorithms to generate texts; Philip M. Parker has written software to automatically generate entire books. Jason Nelson used generative methods with Speech-to-Text software to create a series of digital poems from movies, television and other audio sources.

Live coding

Generative systems may be modified while they operate, for example by using interactive programming environments such as Max/MSP, vvvv, Fluxus, Isadora, Quartz Composer and openFrameworks. This is a standard approach to programming by artists, but may also be used to create live music and/or video by manipulating generative systems on stage, a performance practice that has become known as live coding. As with many examples of software art, because live coding emphasises human authorship rather than autonomy, it may be considered in opposition to generative art.

Theories

Philip Galanter

In the most widely cited theory of generative art, in 2003 Philip Galanter describes generative art systems in the context of complexity theory. In particular the notion of Murray Gell-Mann and Seth Lloyd's effective complexity is cited. In this view both highly ordered and highly disordered generative art can be viewed as simple. Highly ordered generative art minimizes entropy and allows maximal data compression, and highly disordered generative art maximizes entropy and disallows significant data compression. Maximally complex generative art blends order and disorder in a manner similar to biological life, and indeed biologically inspired methods are most frequently used to create complex generative art. This view is at odds with the earlier information theory influenced views of Max Bense and Abraham Moles where complexity in art increases with disorder.

Galanter notes further that given the use of visual symmetry, pattern, and repetition by the most ancient known cultures generative art is as old as art itself. He also addresses the mistaken equivalence by some that rule-based art is synonymous with generative art. For example, some art is based on constraint rules that disallow the use of certain colors or shapes. Such art is not generative because constraint rules are not constructive, i.e. by themselves they don't assert what is to be done, only what cannot be done.

Margaret Boden and Ernest Edmonds

In their 2009 article, Margaret Boden and Ernest Edmonds agree that generative art need not be restricted to that done using computers, and that some rule-based art is not generative. They develop a technical vocabulary that includes Ele-art (electronic art), C-art (computer art), D-art (digital art), CA-art (computer assisted art), G-art (generative art), CG-art (computer based generative art), Evo-art (evolutionary based art), R-art (robotic art), I-art (interactive art), CI-art (computer based interactive art), and VR-art (virtual reality art).

Questions

The discourse around generative art can be characterized by the theoretical questions which motivate its development. McCormack et al. propose the following questions, shown with paraphrased summaries, as the most important:
  1. Can a machine originate anything? Related to machine intelligence - can a machine generate something new, meaningful, surprising and of value: a poem, an artwork, a useful idea, a solution to a long-standing problem?
  2. What is it like to be a computer that makes art? If a computer could originate art, what would it be like from the computer's perspective?
  3. Can human aesthetics be formalized?
  4. What new kinds of art does the computer enable? Many generative artworks do not involve digital computers, but what does generative computer art bring that is new?
  5. In what sense is generative art representational, and what is it representing?
  6. What is the role of randomness in generative art? For example, what does the use of randomness say about the place of intentionality in the making of art?
  7. What can computational generative art tell us about creativity? How could generative art give rise to artifacts and ideas that are new, surprising and valuable?
  8. What characterizes good generative art? How can we form a more critical understanding of generative art?
  9. What can we learn about art from generative art? For example, can the art world be considered a complex generative system involving many processes outside the direct control of artists, who are agents of production within a stratified global art market.
  10. What future developments would force us to rethink our answers?
Another question is of postmodernism—are generative art systems the ultimate expression of the postmodern condition, or do they point to a new synthesis based on a complexity-inspired world-view?
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