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International World Wide Web Conference (WWW)

The Quantum Collective

Lora M. Aroyo, Chris Welty

Published Jan 1, 2018• 1 citation
Publisher

Abstract

AI and collective intelligence systems universally suffer from a deficiency of context. There are innumerable possible contexts that may possibly change the interpretation of some signal, that may change the proper response to some stimulus. For example, an image understanding system that does not recognize an arrest event in a zoomed image of a person's face. How is it possible to know there is more information, outside of what the system can access, that affects the interpretation of data The solution to the context problem in practice today is a pragmatic, engineering one: analyze errors (in recommendations, question answers, image recognition, etc.), classify the kinds of contextual information that caused the wrong behavior, find the most common type of context that causes errors, and add information about that kind of context to the system. Clearly this approach is neither general nor scalable, and ignores the infamous long tail of possible contextual information that may affect a system's understanding and its behavior. In this paper we outline a new, more general, approach to recognizing context. The approach is grounded in a fairly simple intuition: the mathematics underlying quantum mechanics is far more appropriate for modeling, and therefore simulating, human cognitive behavior than the standard toolset from classical statistics. Notions such as Heisenberg's uncertainty principle, superpositions of states, and entanglement have direct and measurable analogs in collective intelligence.

DOI
10.1145/3184558.3191550

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AI and collective intelligence systems universally suffer from a deficiency of context. There are innumerable possible contexts that may possibly change the interpretation of some signal, that may change the proper response to some stimulus. For example, an image understanding system that does not recognize an arrest event in a zoomed image of a person's face. How is it possible to know there is more information, outside of what the system can access, that affects the interpretation of data The solution to the context problem in practice today is a pragmatic, engineering one: analyze errors (in recommendations, question answers, image recognition, etc.), classify the kinds of contextual information that caused the wrong behavior, find the most common type of context that causes errors, and add information about that kind of context to the system. Clearly this approach is neither general nor scalable, and ignores the infamous long tail of possible contextual information that may affect a system's understanding and its behavior. In this paper we outline a new, more general, approach to recognizing context. The approach is grounded in a fairly simple intuition: the mathematics underlying quantum mechanics is far more appropriate for modeling, and therefore simulating, human cognitive behavior than the standard toolset from classical statistics. Notions such as Heisenberg's uncertainty principle, superpositions of states, and entanglement have direct and measurable analogs in collective intelligence.
At first glance, quantum mechanics and behavioural science seem worlds apart -- one rooted in equations and particles, the other in thoughts and choices. Yet, emerging research reveals a profound and unexpected bridge between them. This chapter explores that bridge through quantum models of cognition and decision-making, showing how principles from quantum mechanics can help understand, and even predict, the complexities of human perception, behaviour and societal dynamics. We introduce a computationally accessible framework grounded in quantum theory, designed to model ambiguity, bias and choice in a way that classical logic cannot. Drawing on interdisciplinary sources, we demonstrate how this approach not only enriches our understanding of individual cognition but also extends to AI, video game design, financial behaviour and collective decision-making in society. Through vivid case studies -- from optical illusions in video games to decision biases in economic, defence and political contexts -- we show how quantum models offer new explanatory and predictive power. Thus, this chapter invites readers from all professional backgrounds to reimagine cognition and decision-making, opening new avenues for science, technology and human understanding.
From behavioral sciences to biology to quantum mechanics, one encounters situations where (i) a system outputs several random variables in response to several inputs, (ii) for each of these responses only some of the inputs may "directly" influence them, but (iii) other inputs provide a "context" for this response by influencing its probabilistic relations to other responses. These contextual influences are very different, say, in classical kinetic theory and in the entanglement paradigm of quantum mechanics, which are traditionally interpreted as representing different forms of physical determinism. One can mathematically construct systems with other types of contextuality, whether or not empirically realizable: those that form special cases of the classical type, those that fall between the classical and quantum ones, and those that violate the quantum type. We show how one can quantify and classify all logically possible contextual influences by studying various sets of probabilistic couplings, i.e., sets of joint distributions imposed on random outputs recorded at different (mutually incompatible) values of inputs.
We present critical arguments against individual interpretation of Bohr’s complementarity and Heisenberg’s uncertainty principles. Statistical interpretation of these principles is discussed in the contextual framework. We support the possibility to use Statistical Contextual Realist Interpretation of quantum formalism. In spite of all no-go theorems (e.g., von Neumann, Kochen and Specker,..., Bell,...), recently (quant-ph/0306003 and 0306069) we constructed a realist basis of quantum mechanics. In our model both classical and quantum spaces are rough images of the fundamental prespace. Quantum mechanics cannot be reduced to classical one. Both classical and quantum representations induce reductions of prespace information. ”Often we enter the unknown edifice of a new scientific discipline through a lesser gate that leads us into a side passage. It may take us a long while to find our way to the main portal and to view the whole structure in its proper perspective”. E. Schrödinger 1
How are abstract concepts and musical themes recognized on the basis of some previous experience? It is interesting to compare the different behaviors of human and of artificial intelligences with respect to this problem. Generally, a human mind that abstracts a concept (say, table) from a given set of known examples creates a table-Gestalt: a kind of vague and out of focus image that does not fully correspond to a particular table with well determined features. A similar situation arises in the case of musical themes. Can the construction of a gestaltic pattern, which is so natural for human minds, be taught to an intelligent machine? This problem can be successfully discussed in the framework of a quantum approach to pattern recognition and to machine learning. The basic idea is replacing classical data sets with quantum data sets, where either objects or musical themes can be formally represented as pieces of quantum information, involving the uncertainties and the ambiguities that characterize the quantum world. In this framework, the intuitive concept of Gestalt can be simulated by the mathematical concept of positive centroid of a given quantum data set. Accordingly, the crucial problem "how can we classify a new object or a new musical theme (we have listened to) on the basis of a previous experience?" can be dealt with in terms of some special quantum similarity-relations. Although recognition procedures are different for human and for artificial intelligences, there is a common method of "facing the problems" that seems to work in both cases.
Quantum measurement is inevitable to extract information from quantum systems. Exploring the power and limitation of quantum measurement has profound implications not only to foundational studies, but also to many practical applications. In most of these tasks, many identically prepared quantum systems are measured to acquire sufficient information. Although there is no entanglement or even no classical correlation among these identically systems, surprisingly, quantum collective measurements on them may extract more information than local measurements on individual systems, thereby leading to higher efficiency and precision in these tasks. Although the significance of collective measurements has been recognized for two decades, it is still very challenging to demonstrate their advantage in experiments. Here, we will introduce the first implementation of quantum collective measurement and it's applications on quantum orienting, reducing quantum backation of measurement, and so on.
Context-sensitive behavior can be modeled by enriching an internal state, by allowing a response rule to access context directly, or by preserving a shared state while introducing an auxiliary criterion or control variable. This paper isolates an information-theoretic constraint on the third architecture. Let CCC denote context, SSS a candidate internal or latent state, OOO an observable response, and MMM an auxiliary variable such that O⊥C∣(S,M)O\perp C\mid(S,M)O⊥C∣(S,M). Then I(C;O∣S)≤I(C;M∣S)≤H(M∣S). I(C;O\mid S)\le I(C;M\mid S)\le H(M\mid S). I(C;O∣S)≤I(C;M∣S)≤H(M∣S). Once a shared state has been specified, residual context dependence in behavior therefore lower-bounds both the context information and the conditional entropy that an auxiliary context-mediating mechanism must carry. The bound is representation-relative rather than a measure of state-space size or a universal contextuality measure. A worked recognition-memory example shows how the quantity can be computed for payoff-induced criterion shifts and compared across alternative representational allocations. Ontological contextuality and quantum probability are treated as a separate specialization rather than as prerequisites for the cognitive interpretation. More broadly, the framework provides a basis for analyzing context-memory-control trade-offs in cognitive models and artificial agents that must maintain coherent behavior across changing contexts under bounded internal representations.