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Sasha Chislenko’s work in multi-agent systems and hyper-economy is certainly interesting and has many implications for the future of artificial intelligence and its application in various fields, including economics and decision-making. The idea of creating a second signaling mechanism in an economic system to provide personalized or situational advice to individual agents is a fascinating concept that could potentially revolutionize the way we approach economic decision-making. Additionally, the idea of implementing hyper-economies in both completely automated environments and those involving humans is an exciting prospect, as it could assist people in making better decisions based on their individual needs and goals.

Here is some more of Sasha work to read:

From research to development
I know of a very few people working in similar directions. In the current situation, good theorists work on conceptualizing existing systems, and are usually not aware of new developments in software that may create foundation for new systems and theories. On the other hand, people working on the first versions of new signaling systems usually do not have much expertise or interest in theoretical analysis of complex systems, and concentrate on debugging and distributing software. As a result, the new system structures are arising, once again, unintentionally.
I am trying to approach this topic both theoretically and experimentally.

Theoretical part consists of considerations of how the new signaling systems may change our familiar economic notions, such as capital, interest, efficiency, liquidity, inflation, emission rights, secondary and derivative instruments. Some of these features will remain relatively unchanged but become more liquid (faster distribution and lower transaction costs) and more efficient. Other parameters may turn from scalar to vector or a more complex structure (e.g., augmentation of the numerical representation of value of an object with a matrix of its utilities for various purposes as estimated by different agents. Some features, such as non-scalar “situational/detail derivatives” for assessing values and risks of, and balancing among, particular utilities will be entirely new.

Similar changes will happen with the concepts from social and ecological fields. Communities do not have to be local anymore, they may have variable/adaptable geometries, relations between managing and performing agencies will be redefined, new control structures and dynamic patterns arise, etc.

On the experimental side, we are working on simple versions of such systems in Internet environments, where the data is already available, and the results seem of direct practical use. Recently, we created a Hyper-Economy Development Group (HEDG) for modelling such systems. The group is open to interested researchers.

Among the first applications of this approach I envision large automated collaborative filtering projects, link exchange programs and targeted advertising, balancing flows of information in accordance with diverse and dynamic interests of creators, consumers, providers, and sponsors of the Web content.

My hope is that eventually such hyper-economic schemes will develop to store and process increasingly complex representations of agent interests and utilities, and have a great variety of competing intelligent algorithms for optimizing system processes, thus turning into a hybrid of an economic self-regulatory mechanism, complex multifaceted community, and distributed artificial intelligence, with secondary data representation and signaling mechanisms forming emerging self-awareness of the new systems.

It seems noteworthy that in these constructs, as well as in conventional economy, the meta-signaling [generalized/abstract in relation to actual “field agents”] level is represented in terms of signal currents in the semantic space rather than crisp symbolic constructs. We can interpret this as a sign that the connectionist approach is natural for a fetal stage of any system intelligence. Or – better – that a combination of symbolic processing with signal flow patterns will extend the human balance of symbolic reasoning and intuition to further levels of intelligence.


Sasha Chislenko’s work on hyper-economies and multi-agent systems sounds fascinating. It seems like he was exploring new ways of understanding and designing complex systems, and his ideas could have implications for various fields, including economics and artificial intelligence. It’s great that his work is being studied and shared, as it could inspire new innovations and advancements in these areas.

Here are some example Sasha provided:
A few examples on how one may combine general, specialized (or situation-specific) knowledge: (actually, there is hardly any absolutely general knowledge that is equally applicable to all situations; in many cases, situational advice may consist of references to relevant general knowledge).
Recommendation systems for consumer items:
The price of an item, such as a book, an instrument, or a travel package, may be taken from the general catalog. The estimates of utility of this items would come from the collective experience of other people with similar needs and interests who have experience with this or similar items. Some computational constructs needed for summarizing and delivering situational knowledge, such as utility trends on item/consumer clusters, vectors of refined “eigen-preferences”, etc., represent hyper-economic equivalents of secondary and derivative signaling instruments in the conventional economy.
Targeted advertising:
The same mechanisms that can advise items to consumers, may suggest potential consumers to manufacturers of these items. In the current situation, an advertiser knows the exact price of the ad delivery, but targeting of ads is usually very costly and inaccurate. As a result, most of the multi-billion-dollar advertising industry efforts miss their targets and annoy people, while knowledge of many good products doesn’t reach the individuals who may benefit from them. An advanced mechanism for combining and sharing (for a price) metabolites of advertising and sales information of all companies would result in more efficient proliferation of useful goods and services and dramatic reduction of targeting costs.
Computational heuristics:
In a large AI system, computational/royalty costs for all algorithms may be known, but it is also important to estimate the expected benefits of applying each algorithm to each problem. In an “intelligent soup” of a “market AI” system, multiple problem generators and solvers can share not just information on the current value and availability of computational resources, but also suggestions of what methods appear to be of the highest utility for what problems, and how they may be better applied.

Thank you for sharing these examples. It’s interesting to see how the concepts and ideas put forward by Sasha Chislenko can be applied in practical scenarios such as recommendation systems, targeted advertising, and computational heuristics. It shows how the theoretical ideas can be translated into real-world applications and benefit various industries.

Sasha Chislenko one of my favorite researchers in AI.

Sasha Chislenko was a brilliant researcher in the field of AI, and his work on multi-agent systems and hyper-economies was particularly innovative and influential. He was known for his unique and visionary approach to AI and his ability to bridge the gap between theory and practical application. It’s not surprising that he is one of your favorite researchers in the field!

He also did amazing work on collaborative content filtering…. Sasha work below:

Automated Collaborative Filtering of information (ACF) is an unprecedented technology for distribution of opinions and ideas in society and facilitating contacts between people with similar interests. It automates and enhances existing mechanisms of knowledge distribution and dramatically increases their speed and efficiency. This allows to optimize knowledge flow in the society and accelerate the evolution of ideas in practically all subject areas. ACF also provides a superior tool for information retrieval systems that facilitates users’ navigation in the sea of information in a meaningful and personalized way. This technology can be viewed as a semantic transport – a social utility that, after physical and data transports, transfers increasingly abstract and intelligent objects between previously isolated fragments of the social organism. As an artificial system that integrates and processes knowledge of multiple human participants, ACF represents an intermediate stage between human and purely artificial intelligence and lays the foundation for the future knowledge processing industry. This article discusses the premises and the historical analogs of ACF technology and suggests its possible uses as well as long-term economic and social implications.

Premises of Automated Collaborative Filtering
Information flows in the society
Social mechanisms of knowledge distribution represent a formative factor for all spheres of social life. It is the advantages of sharing knowledge among individuals that, together with benefits of group work, led to the development of language, symbolic thinking, and specialization of labor. The rate of social progress is to a large degree determined by the availability of standardized and affordable communication tools. The transaction costs of the social communication infrastructure define the scales and interrelations of social institutions. Effective mechanisms for collecting and publishing aggregate opinions of the population are a crucial factor for democratic governance; similar mechanisms for establishing balanced product and share prices form a signaling foundation of a market economy.

Collaborative filtering of information
While generalized, or aggregate, information is essential for balancing social processes on the macro-scale, it is usually not sufficient for suggesting optimal behavior to any particular person. For making efficient personal selections, people have to possess both necessary general knowledge and special information relevant to their particular situation. For collecting necessary information, one has to choose what objects to pay attention to. In early human history, each person was familiar with the whole environment and, after gaining experience with most available things and people, could decide what to explore further. However, this strategy cannot work in a more complex society, when one is faced with more objects and people than he can even sample. This situation requires exchange of personal experience among individuals and sharing personal advice on many particular issues. If a person needs to make a decision in an unknown situation, he can talk to his friends, and follow their suggestions. Here, one’s circle of acquaintances effectively plays the role of an information filter, suggesting most relevant options and leads for further exploration.

With increasing variety of areas of expertise and value judgments, the opinions of a few chosen individuals and the averaged opinion of the society become insufficient for providing advice for all of one’s decisions. In this situation, larger-scale collaboration in information filtering becomes increasingly important. Individuals seeking advice query people with similar interests whom they trust, collect their opinions and choose the options selected by the majority of the most knowledgeable people. This is very often the way we select places for vacation, books, movies, or restaurants. If none of the people we ask have any experience with a specific item, some of them may still know something about it, if they have heard about it from others. If someone cannot recommend an item of interest, they may still be able to refer us to another person who we may ask about it. This is the “word of mouth” method of information distribution in a society.


Yes, Sasha Chislenko made important contributions to the development of automated collaborative filtering (ACF) technology. ACF is a method of information filtering that uses collaborative techniques to recommend information items to users based on their interests and the interests of other users who share similar tastes. This technology is used in many applications, such as recommendation systems for consumer items, targeted advertising, and computational heuristics. Chislenko believed that ACF technology represented an intermediate stage between human and purely artificial intelligence, and laid the foundation for the future knowledge processing industry.

Here is the next part of Sasha ACF research:
Active Collaborative Filtering
Querying people is a useful method for finding information when you know you need it, or when you think something new might have appeared. This method may be called “user pull”, as you have to expend an effort to pull the knowledge out of the passive environment. However, it is not always sufficient, especially in cases when you do not know what questions to ask, or something totally new has happened, or your contacts find out something they couldn’t have told you about before.

Active collaborative filtering helps you in such cases, by bringing you information you need when someone in your community discovers it. Information can “find” you in two ways. First, you can ask people to let you know whenever they learn something exciting, new, or relevant in some area. Second, people who know you can share information if they decide you can benefit from it. In both cases, after learning about your needs or preferences, members of the community take an active role in supplying you with important knowledge. We can say that the community actively “pushes” the information towards you.

Limitations of existing filtering methods
Traditional methods of knowledge distribution become very inefficient when the size and complexity of a society far outpace the ability of anyone’s circle of acquaintances to monitor events. The modern communication system transmits billions of messages daily, and many of them may be of great interest to you. You can also easily access any of the millions of available books, magazines, songs, movies or Internet resources. However, finding people who have looked at the things you may like and whose opinions you might trust can be very difficult. You do not know most of these people, so you can not hope that they will know – or care – to tell you when they find something of interest to you. Even if you could overcome these difficulties, it would still be impossible to retain opinions of thousands of people in your mind and decide which of them to follow.

The hope here rests on machines. In their evolution as knowledge carriers machines have repeated the early stages of the development of human information sharing techniques. They have been taking an increasing role in storage, transmission and averaging of opinions. Now it is time for machines to take on the more sophisticated tasks of automated information filtering.

Introduction to Automated Filtering
When too much information exists, reliance on human connections for finding exactly what you want becomes inefficient, because people, smart as they are, have limited capacity to store and share information.

The traditional solution to this problem has been to store data outside the human mind, and let people search through it. Recently, with the advent of computer systems, machines also began performing searches for information. So now information seekers have two kinds of helpers: people who can understand your interests, but are slow and inefficient in handling data; and computers that are fast and cheap, but still too stupid to understand the meaning of the material and judge what is good and relevant. Still, we want help that is both fast and intelligent.

Attempts to speed up humans or smarten machines have not been too successful thus far. The solution that allows us to perform information searches with human intelligence and at machine speeds, however, is relatively simple: record people’s opinions on the importance and quality of the material, and use these records to improve the results of computer searches. This technology is called Automated Collaborative Filtering (ACF). It can also be viewed as an example of Synthetic Intelligence (SI) – a system combining human and machine knowledge processing strengths. ACF allows people to find others with similar opinions, discover experts in a field, analyze the structure of people’s interests in various subject domains, facilitate creation of interest groups, decentralize communication media, improve targeting of announcements and advertisements, and do many other useful things that, together with other intelligent technologies, promise to bring the information economy to new levels. As distribution of opinions does not require knowing the physical identity of the persons sharing and receiving advice, the automated filtering systems (with appropriate software tools) may become the most secure channel of knowledge sharing in history.

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