Behavioural Economics: Classical and Modern

In this paper, the origins and development of behavioural economics, beginning with the pioneering works of Herbert Simon (1953) and Ward Edwards (1954), is traced, described and (critically) discussed, in some detail. Two kinds of behavioural economics – classical and modern – are attributed, respectively, to the two pioneers. The mathematical foundations of classical behavioural economics is identified, largely, to be in the theory of computation and computational complexity; the corresponding mathematical basis for modern behavioural economics is, on the other hand, claimed to be a notion of subjective probability (at least at its origins in the works of Ward Edwards). The economic theories of behavior, challenging various aspects of 'orthodox' theory, were decisively influenced by these two mathematical underpinnings of the two theoriesClassical Behavioural Economics, Modern Behavioural Economics, Subjective Probability, Model of Computation, Computational Complexity. Subjective Expected Utility

Computational Behavioural Economics

Both behavioral economics and computational intelligence (machine learning) rely on the extensive use of heuristics to address decision-making problems in an ill-defined and ill-structured environment. While the former has a focus on behaviors, and the other has a focus on the algorithms, this distinction is merely superficial. The real connection between the two is that through algorithmic procedure the latter provides the former with the computational underpinnings of the decision-making processes. In this chapter, we review this connection, dubbed computational behavioral economics. To do so, we review a number of frequently-used computational intelligence tools in the realm of computational economics, including K nearest neighbors, K means, self-organizing maps, reinforcement learning, decision trees, evolutionary computation, swarm intelligence, and “random” behavior. This review enables us to see how the heuristics employed in the latter, such as closeness, similarity, smoothness, default, automation, hierarchy, and modularity can lay a computational foundation of the heuristics studied by the former

On Complex Economic Dynamics: Agent-Based Computational Modeling and Beyond

This chapter provides a selective overview of the recent progress in the 6 study of complex adaptive systems. A large part of the review is attributed to agent- 7 based computational economics (ACE). In this chapter, we review the frontier of 8 ACE in light of three issues that have long been grappled with, namely financial 9 markets, market processes, and macroeconomics. Regarding financial markets, 10 we show how the research focus has shifted from trading strategies to trading 11 institutions, and from human traders to robot traders; as to market processes, we 12 empathetically point out the role of learning, information, and social networks 13 in shaping market (trading) processes; finally, in relation to macroeconomics, we 14 demonstrate how the competition among firms in innovation can affect the growth 15 pattern. A minor part of the review is attributed to the recent econometric computing, 16 and methodology-related developments which are pertinent to the study of complex 17 adaptive systems

Fabrication and characterization of a novel konjac glucomannan-based air filtration aerogels strengthened by wheat straw and okara

The konjac glucomannan (KGM)-based aerogel as an air filtration material was fabricated through sol-gel and freeze-drying methods. Results showed that gelatin and starch addition could increase the filtration efficiency and compressive strength of aerogel significantly, due to the appearance of more microporous structure and the formation of dense structure in aerogel. The addition of wheat straw could decrease the filtration resistance and increase the breathability of KGM-based aerogel, which was attributed to the multi-cavities of wheat straw. The aerogel with wheat straw had a filtration efficiency of 93.54% for particle matters ≥ 0.3 μm, a filtration resistance 29 Pa, and an air permeability 271.42 L/s·m2. Okara addition could increase the hydrophobicity of KGM-based aerogel by increasing the water contact angle and decreasing the equilibrium water content. The water contact angle of the aerogel containing okara reached 105.4°, and the equilibrium water content was decreased by 17.03%–81.10% compared with that without okara, with relative humidity 0%–80%. The results demonstrated that the KGM-based aerogel had good performance on filtration, mechanical and hydrophobic properties, indicating high potential application as an air filtration material

Lobocrassins A–E: New Cembrane-Type Diterpenoids from the Soft Coral Lobophytum crassum

Five new cembrane-type diterpenoids, lobocrassins A–E (1–5), were isolated from the soft coral Lobophytum crassum. The structures of cembranes 1–5 were established by spectroscopic and chemical methods and by comparison of the spectral data with those of known cembrane analogues. Lobocrassin A (1) is the first cembranoid possessing an α-chloromethyl-α-hydroxy-γ-lactone functionality and is the first chlorinated cembranoid from soft corals belonging to the genus Lobophytum. Lobocrassins B (2) and C (3) were found to be the stereoisomers of the known cembranes, 14-deoxycrassin (6) and pseudoplexaurol (7), respectively. Lobocrassin B (2) exhibited modest cytotoxicity toward K562, CCRF-CEM, Molt4, and HepG2 tumor cells and displayed significant inhibitory effects on the generation of superoxide anion and the release of elastase by human neutrophils

Emerged HA and NA Mutants of the Pandemic Influenza H1N1 Viruses with Increasing Epidemiological Significance in Taipei and Kaohsiung, Taiwan, 2009–10

The 2009 influenza pandemic provided an opportunity to observe dynamic changes of the hemagglutinin (HA) and neuraminidase (NA) of pH1N1 strains that spread in two metropolitan areas -Taipei and Kaohsiung. We observed cumulative increases of amino acid substitutions of both HA and NA that were higher in the post–peak than in the pre-peak period of the epidemic. About 14.94% and 3.44% of 174 isolates had one and two amino acids changes, respective, in the four antigenic sites. One unique adaptive mutation of HA2 (E374K) was first detected three weeks before the epidemic peak. This mutation evolved through the epidemic, and finally emerged as the major circulated strain, with significantly higher frequency in the post-peak period than in the pre-peak (64.65% vs 9.28%, p<0.0001). E374K persisted until ten months post-nationwide vaccination without further antigenic changes (e.g. prior to the highest selective pressure). In public health measures, the epidemic peaked at seven weeks after oseltamivir treatment was initiated. The emerging E374K mutants spread before the first peak of school class suspension, extended their survival in high-density population areas before vaccination, dominated in the second wave of class suspension, and were fixed as herd immunity developed. The tempo-spatial spreading of E374K mutants was more concentrated during the post–peak (p = 0.000004) in seven districts with higher spatial clusters (p<0.001). This is the first study examining viral changes during the naïve phase of a pandemic of influenza through integrated virological/serological/clinical surveillance, tempo-spatial analysis, and intervention policies. The vaccination increased the percentage of E374K mutants (22.86% vs 72.34%, p<0.001) and significantly elevated the frequency of mutations in Sa antigenic site (2.36% vs 23.40%, p<0.001). Future pre-vaccination public health efforts should monitor amino acids of HA and NA of pandemic influenza viruses isolated at exponential and peak phases in areas with high cluster cases

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3, reports on nine research projects and a list of publications.National Aeronautics and Space Administration Contract 958461U.S. Navy - Office of Naval Research Grant N00014-92-J-1616University of California/Jet Propulsion Laboratory Contract 960408U.S. Army - Corps of Engineers/Cold Regions Research and Engineering Laboratory Contract DACA89-95-K-0014Mitsubishi CorporationU.S. Navy - Office of Naval Research Agreement N00014-92-J-4098Federal Aviation AdministrationDEMACOJoint Services Electronics Program Grant DAAHO4-95-1-003