Information Dissemination and Aggregation in Asset Markets with Simple Intelligent Traders

Various studies of asset markets have shown that traders are capable of learning and transmitting information through prices in many situations. In this paper we replace human traders with intelligent software agents in a series of simulated markets. Using these simple learning agents, we are able to replicate several features of the experiments with human subjects, regarding (1) dissemination of information from informed to uninformed traders, and (2) aggregation of information spread over different traders.

Synthesis and Aggregation Behavior of Pluronic F87/Poly(acrylic acid) Block Copolymer with Doxorubicin

Poly(acrylic acid) (PAA) was grafted onto both termini of Pluronic F87 (PEO₆₇-PPO₃₉-PEO₆₇) via atom transfer radical polymerization to produce a novel muco-adhesive block copolymer PAA₈₀-b-F₈₇-b-PAA₈₀. It was observed that PAA₈₀-F₈₇-PAA₈₀ forms stable complexes with weakly basic anti-cancer drug, Doxorubicin. Thermodynamic changes due to the drug binding to the copolymer were assessed at different pH by isothermal titration calorimetry (ITC). The formation of the polymer/drug complexes was studied by turbidimetric titration and dynamic light scattering. Doxorubicin and PAA-b-F87-b-PAA block copolymer are found to interact strongly in aqueous solution via non-covalent interactions over a wide pH range. At pH>4.35, drug binding is due to electrostatic interactions. Hydrogen-bond also plays a role in the stabilization of the PAA₈₀-F₈₇-PAA₈₀/DOX complex. At pH 7.4 (α=0.8), the size and stability of polymer/drug complex depend strongly on the doxorubicin concentration. When CDOX <0.13mM, the PAA₈₀-F₈₇-PAA₈₀ copolymer forms stable inter-chain complexes with DOX (110 ~ 150 nm). When CDOX >0.13mM, as suggested by the light scattering result, the reorganization of the polymer/drug complex is believed to occur. With further addition of DOX (CDOX >0.34mM), sharp increase in the turbidity indicates the formation of large aggregates, followed by phase separation. The onset of a sharp enthalpy increase corresponds to the formation of a stoichiometric complex.

Switching Between Discrete and Continuous Process Models to Predict Molecular Genetic Activity

Two kinds of process models have been used in programs that reason about change: Discrete and continuous models. We describe the design and implementation of a qualitative simulator, PEPTIDE, which uses both kinds of process models to predict the behavior of molecular energetic systems. The program uses a discrete process model to simulate both situations involving abrupt changes in quantities and the actions of small numbers of molecules. It uses a continuous process model to predict gradual changes in quantities. A novel technique, called aggregation, allows the simulator to switch between theses models through the recognition and summary of cycles. The flexibility of PEPTIDE's aggregator allows the program to detect cycles within cycles and predict the behavior of complex situations.

KAM: Automatic Planning and Interpretation of Numerical Experiments Using Geometrical Methods

KAM is a computer program that can automatically plan, monitor, and interpret numerical experiments with Hamiltonian systems with two degrees of freedom. The program has recently helped solve an open problem in hydrodynamics. Unlike other approaches to qualitative reasoning about physical system dynamics, KAM embodies a significant amount of knowledge about nonlinear dynamics. KAM's ability to control numerical experiments arises from the fact that it not only produces pictures for us to see, but also looks at (sic---in its mind's eye) the pictures it draws to guide its own actions. KAM is organized in three semantic levels: orbit recognition, phase space searching, and parameter space searching. Within each level spatial properties and relationships that are not explicitly represented in the initial representation are extracted by applying three operations ---(1) aggregation, (2) partition, and (3) classification--- iteratively.

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.

Association Behavior of Biotinylated and Non-Biotinylated PolyEthylene Oxide-b-Poly(2-(Diethylamino)Ethyl Methacrylate)

Biotinylated and non-biotinylated copolymers of ethylene oxide (EO) and 2-(diethylamino)ethyl methacrylate (DEAEMA) were synthesized by the atom transfer radical polymerization technique (ATRP). The chemical compositions of the copolymers as determined by NMR are represented by PEO₁₁₃PDEAEMA₇₀ and biotin-PEO₁₀₄PDEAEMA₉₃ respectively. The aggregation behavior of these polymers in aqueous solutions at different pHs and ionic strengths was studied using a combination of potentiometric titration, dynamic light scattering (DLS), static light scattering (SLS), and transmission electron microscopy (TEM). Both PEO-b-PDEAEMA and biotin-PEO-b-PDEAEMA diblock copolymers form micelles at high pH with hydrodynamic radii (Rh) of about 19 and 23 nm, respectively. At low pH, the copolymers are dispersed as unimers in solution with Rh of about 6-7 nm. However, at a physiological salt concentration (cs) of about 0.16M NaCl and a pH of 7-8, the copolymers form large loosely packed Guassian chains, which were not present at the low cs of 0.001M NaCl. The critical micelle concentrations (CMC) and the cytotoxicity of the copolymers were investigated to determine a suitable polymer concentration range for future biological applications. Both PEO-b-PDEAEMA and biotin-PEO-b-PDEAEMA diblock copolymers possess identical CMC values of about 0.0023 mg/g, while the cytotoxicity test indicated that the copolymers are not toxic up to 0.05mg/g (> 83% cell survival at this concentration).

Molecular computations for reactions and phase transitions: applications to protein stabilization, hydrates and catalysis

In this work we have made significant contributions in three different areas of interest: therapeutic protein stabilization, thermodynamics of natural gas clathrate-hydrates, and zeolite catalysis. In all three fields, using our various computational techniques, we have been able to elucidate phenomena that are difficult or impossible to explain experimentally. More specifically, in mixed solvent systems for proteins we developed a statistical-mechanical method to model the thermodynamic effects of additives in molecular-level detail. It was the first method demonstrated to have truly predictive (no adjustable parameters) capability for real protein systems. We also describe a novel mechanism that slows protein association reactions, called the “gap effect.” We developed a comprehensive picture of methioine oxidation by hydrogen peroxide that allows for accurate prediction of protein oxidation and provides a rationale for developing strategies to control oxidation. The method of solvent accessible area (SAA) was shown not to correlate well with oxidation rates. A new property, averaged two-shell water coordination number (2SWCN) was identified and shown to correlate well with oxidation rates. Reference parameters for the van der Waals Platteeuw model of clathrate-hydrates were found for structure I and structure II. These reference parameters are independent of the potential form (unlike the commonly used parameters) and have been validated by calculating phase behavior and structural transitions for mixed hydrate systems. These calculations are validated with experimental data for both structures and for systems that undergo transitions from one structure to another. This is the first method of calculating hydrate thermodynamics to demonstrate predictive capability for phase equilibria, structural changes, and occupancy in pure and mixed hydrate systems. We have computed a new mechanism for the methanol coupling reaction to form ethanol and water in the zeolite chabazite. The mechanism at 400°C proceeds via stable intermediates of water, methane, and protonated formaldehyde.