Song-Ju Kim

Question order effects show that people may answer differently when two questions are asked in different orders. In quantum cognition, the Wang-Busemeyer QQ equality is a striking binary constraint on such effects. This paper gives a requirement-first interpretation of that constraint. The two orders (AB) and (BA) are treated not as histories in a fixed temporal arena, but as local order-forms generated under the same finite-state requirements. In the minimal two-question, two-answer case, a common bidirectional gluing kernel between these order-forms is forced to have a reciprocal binary form, and the equality follows. Thus, the result is interpreted as a fixed-form signature of reciprocal binary order-form gluing. The framework also predicts that beyond two answers, contextual order formation should appear as contrast-specific gluing and possible holonomy-like mismatch, rather than as a single higher-dimensional equality.

Agents often need to behave coherently across multiple contexts while maintaining a unified internal state. We propose a minimal model of context integration in which contexts are treated not as labels but as interventions that can modify outcome statistics. We assume that the agent maintains a single semantically stable internal state whose meaning does not fragment across contexts. Under this constraint, we argue that contextual probabilistic structure is not an anomaly but a functional requirement for integrating multiple contexts without duplicating internal representations. This reframes contextuality as a design principle for minimal-memory agents. We derive qualitative predictions concerning order effects and memory–context tradeoffs, and discuss implications for cognitive and artificial systems.

Decision making often exhibits context dependence that challenges classical probability theory. While quantum cognition has successfully modeled such phenomena, it remains unclear whether quantum probability is merely a convenient assumption or a necessary consequence of decision dynamics. Here we present a theoretical framework in which contextuality arises generatively from physically grounded constraints on decision making. By developing a quantum extension of the Tug-of-War (TOW) model, we show that conservation-based internal state updates and measurement-induced disturbance preclude any non-contextual classical description with a single, unified internal state. Contextuality therefore emerges as a structural consequence of adaptive learning dynamics. We further show that the resulting measurement structure admits Klyachko-Can-Binicioglu-Shumovsky (KCBS)-type contextuality witnesses in a minimal single-system setting. These results indicate that quantum probability is not merely a descriptive convenience, but an unavoidable effective theory for adaptive decision dynamics.

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it’s especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments.

The design of medium access control protocols for the Internet of Things (IoT) has become an important issue due to the rapid growth in the number of IoT devices. Autonomous distributed time division multiple access (TDMA) has been designed in previous research, but not for IoT devices. In this study, we propose a decentralized TDMA for massiveIoT networks by applying the desynchronization phenomena of nonlinear coupled oscillators. The proposed scheme is designed and implemented on actual IoT devices. The results of numerical simulations and actual experiments using IoT devices demonstrate that the timings of packet transmission of the IoT devices can be desynchronized like a TDMA system for both cases static and dynamic networks.

In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.

In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.

We propose a simple channel-allocation method based on tug-of-war (TOW) dynamics, combined with the time scheduling based on nonlinear oscillator synchronization to efficiently use of the space (channel) and time resources in wireless communications. This study demonstrates that synchronization groups, where each node selects a different channel, are non-uniformly distributed in phase space such that every distance between groups is larger than the area of influence. New type of self-organized spatiotemporal patterns can be formed for resource allocation according to channel rewards.