On Iterative Proportional Updating: Limitations and Improvements for General Population Synthesis.

Population synthesis is the foundation of the agent-based social simulation. Current approaches mostly consider basic population and households, rather than other social organizations. This article starts with a theoretical analysis of the iterative proportional updating (IPU) algorithm, a representative method in this field, and then gives an extension to consider more social organization types. The IPU method, for the first time, proves to be unable to converge to an optimal population distribution that simultaneously satisfies the constraints from individual and household levels. It is further improved to a bilevel optimization, which can solve such a problem and include more than one type of social organization. Numerical simulations, as well as population synthesis using actual Chinese nationwide census data, support our theoretical conclusions and indicate that our proposed bilevel optimization can both synthesize more social organization types and get more accurate results.

Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics.

Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.

TiDEC: A Two-Layered Integrated Decision Cycle for Population Evolution.

Agent-based simulation is a useful approach for the analysis of dynamic population evolution. In this field, the existing models mostly treat the migration behavior as a result of utility maximization, which partially ignores the endogenous mechanisms of human decision making. To simulate such a process, this article proposes a new cognitive architecture called the two-layered integrated decision cycle (TiDEC) which characterizes the individual's decision-making process. Different from the previous ones, the new hybrid architecture incorporates deep neural networks for its perception and implicit knowledge learning. The proposed model is applied in China and U.S. population evolution. To the best of our knowledge, this is the first time that the cognitive computation is used in such a field. Computational experiments using the actual census data indicate that the cognitive model, compared with the traditional utility maximization methods, cannot only reconstruct the historical demographic features but also achieve better prediction of future evolutionary dynamics.

A Survey of Cognitive Architectures in the Past 20 Years.

Building autonomous systems that achieve human level intelligence is one of the primary objectives in artificial intelligence (AI). It requires the study of a wide range of functions robustly across different phases of human cognition. This paper presents a review of agent cognitive architectures in the past 20 year's AI research. Different from software structures and simulation environments, most of the architectures concerned are established from mathematics and philosophy. They are categorized according to their knowledge processing patterns-symbolic, emergent or hybrid. All the relevant literature can be accessed publicly, particularly through the Internet. Available websites are also summarized for further reference.

A negative feedback loop between miR-200b and the nuclear factor-κB pathway via IKBKB/IKK-ß in breast cancer cells.

MicroRNAs (miRNAs) act as important post-transcriptional regulators of gene expression in diverse signalling pathways. However, the relationship between miR-200b and the nuclear factor-κB (NF-κB) signalling pathway remains poorly understood in breast cancer cells. In the current study, we show that IKBKB is a direct target of miR-200b, and that miR-200b downregulates IKBKB expression via directly binding to its 3'-UTR. miR-200b inhibits IκBα phosphorylation, nuclear p50/p65 expression, NF-κB-binding activity, and the translocation of p65 to the nucleus. In addition, miR-200b also suppresses tumour necrosis factor (TNF)-α-induced NF-κB activation and the expression of NF-κB target genes. Importantly, IKBKB overexpression attenuates the inhibitory roles of miR-200b in NF-κB expression, NF-κB-binding activity, and the nuclear translocation of p65. We also show that NF-κB p65 knockdown reduces the binding of NF-κB to the miR-200b promoter and miR-200b promoter activity. Furthermore, p65 knockdown or inhibition of IκBα phosphorylation suppresses miR-200b expression. Finally, functional studies show that IKBKB overexpression can restore the cell growth and migration that are suppressed by miR-200b. In conclusion, our results demonstrate that miR-200b, a transcriptional target of NF-κB, suppresses breast cancer cell growth and migration, and NF-κB activation, through downregulation of IKBKB, indicating that miR-200b has potential as a therapeutic target in breast cancer patients.