An Evolutionary Framework for Determining Heterogeneous Strategies in Multi-Agent Marketplaces

We propose an evolutionary approach for studying the dynamics of interaction of strategic agents that interact in a marketplace. The goal is to learn which agent strategies are most suited by observing the distribution of the agents that survive in the market over extended periods of time. We present experimental results from a simulated market, where multiple service providers compete for customers using different deployment and pricing schemes. The results show that heterogeneous strategies evolve and co-exist in the same market.marketing;simulation;multi-agent systems;complexity economics;trading agents

Microsimulation Models to Inform Colorectal Cancer Screening Decisions: From validated tools to tailoring recommendations

The aim of this thesis is to first describe the steps that are required to standardize the structure of a microsimulation model (as MISCAN-Colon) and make it as the core of an online user-friendly model application. Briefly, these steps include: i) assessing effectiveness of CRC screening in different screening settings; ii) validating the model structure and its assumptions; and iii) building an online user-friendly platform that allow users to easily upload country-specific data, adjust a model, and simulate future outcomes of CRC screening in their countries. Subsequently, this thesis aims to demonstrate how models can be used to help policymakers in their decisions about CRC screening regarding populations at different risk of CRC

Spatial & Temporal Characteristics of Ha flares during the period 1975-2002 (comparison with SXR flares)

Although the energetic phenomena of the Sun (flares, coronal mass injections etc.) exhibit intermittent stochastic behavior in their rate of occurrence, they are well correlated to the variations of the solar cycle. In this work we study the spatial and temporal characteristics of transient solar activity in an attempt to statistically interpret the evolution of these phenomena through the solar cycle, in terms of the self-organized criticality theory.Comment: Recent Advances in Astronomy and Astrophysics: 7th International Conference of the Hellenic Astronomical Society. AIP Conference Proceedings, Volume 848, pp. 194-198 (2006

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents

Two-dimensional imaging of the spin-orbit effective magnetic field

We report on spatially resolved measurements of the spin-orbit effective magnetic field in a GaAs/InGaAs quantum-well. Biased gate electrodes lead to an electric-field distribution in which the quantum-well electrons move according to the local orientation and magnitude of the electric field. This motion induces Rashba and Dresselhaus effective magnetic fields. The projection of the sum of these fields onto an external magnetic field is monitored locally by measuring the electron spin-precession frequency using time-resolved Faraday rotation. A comparison with simulations shows good agreement with the experimental data.Comment: 6 pages, 4 figure

Social norms and e-motions in problematic social media use among adolescents

© 2020 The Authors Introduction: Being constantly connected on social media is a “way of being” among adolescents. However, social media use can become “problematic” for some users and only a few studies have explored the concurrent contribution of social context and emotion regulation to problematic social media use. The current study aimed to test: (i) the influence of friends (i.e., their social media use and group norms about social media use); and (ii) the effects of difficulties in emotion regulation and so-called “e-motions” on adolescents’ problematic social media use. Methods: A cross-sectional study was conducted in Italian secondary schools. An online questionnaire was administered to 761 adolescents (44.5% females; Mage = 15.49 years; SDage = 1.03). Results: Path analysis showed that social norms were directly associated with problematic social media use and friends’ social media use was associated with the frequency of social media use, which, in turn, was associated with problematic use. Difficulties in emotion regulation were directly and indirectly linked to problematic social media use via frequency of use and facilitating use of e-motions. Conclusions: These findings provide support for the importance of both peer influence and emotion regulation in this context. Social norms and emotion regulation should be considered in prevention programs addressing problematic social media use in adolescents

photochemical performance of carpobrotus edulis in response to various substrate salt concentrations

Abstract Substrate salinity is one of the main abiotic factors limiting plant establishment, growth and distribution in coastal habitats. Nevertheless, few studies have investigated the interaction between salt concentration and duration of exposure on the physiology and growth of Carpobrotus edulis, an important invasive plant species growing in coastal dune habitats. In this study, four salinity treatment cycles of different length (three, six, twelve and twenty-four days) at salinity of 0 M, 0.1 M, 0.2 M and 0.3 M were imposed. A significant response in plant growth was elicited after 24 days of treatment. The main shoot length (MSL) and stem biomass (SBMS) increased by 11% and 4%, respectively at 0.1 M and by 25% and 6% at 0.2 M compared with the control. At 0.3 M MSL did not significantly differ from the control while SBMS was 18% lower. Moreover, C. edulis showed a high photoprotection mechanism efficiency resulting in a high carotenoid to chlorophyll ratio increase which was two, three and four times higher than the control at 0.1 M, 0.2 M and 0.3 M, respectively. Photochemically, the quantum yield of photosynthesis (ΦPSII) was 17%, 50% and 52% lower than the control at 0.1 M, 0.2 M and 0.3 M. The ΦPSII decrease was associated with a low leaf nitrogen content (NL) decrease (16%, 21% lower than the control at 0.1 M and 0.2 M, respectively). By contrast, NL had the highest decrease (41% lower than the control) at 0.3 M, which constrains the growth capacity. Overall, C. edulis was able to modulate its response to salinity. The salt stimulated shoot elongation at low or moderate salt concentrations could confer a competitive advantage making C. edulis even more efficient in establishing within the areas which it colonizes. Since the expansion of C. edulis may be enhanced by the forecasted increase in soil salinity, it will be of paramount importance to apply effective management practices in areas invaded by C. edulis to limit its expansion and preserve the native plant biodiversity