Single-trial visually evoked potentials predict both individual choice and market outcomes

A central assumption in the behavioral sciences is that choice behavior generalizes enough across individuals that measurements from a sampled group can predict the behavior of the population. Following from this assumption, the unit of behavioral sampling or measurement for most neuroimaging studies is the individual; however, cognitive neuroscience is increasingly acknowledging a dissociation between neural activity that predicts individual behavior and that which predicts the average or aggregate behavior of the population suggesting a greater importance of individual differences than is typically acknowledged. For instance, past work has demonstrated that some, but not all, of the neural activity observed during value-based decision-making is able to predict not just individual subjects’ choices but also the success of products on large, online marketplaces—even when those two behavioral outcomes deviate from one another—suggesting that some neural component processes of decision-making generalize to aggregate market responses more readily across individuals than others do. While the bulk of such research has highlighted affect-related neural responses (i.e. in the nucleus accumbens) as a better predictor of group-level behavior than frontal cortical activity associated with the integration of more idiosyncratic choice components, more recent evidence has implicated responses in visual cortical regions as strong predictors of group preference. Taken together, these findings suggest a role of neural responses during early perception in reinforcing choice consistency across individuals and raise fundamental scientific questions about the role sensory systems in value-based decision-making processes. We use a multivariate pattern analysis approach to show that single-trial visually evoked electroencephalographic (EEG) activity can predict individual choice throughout the post-stimulus epoch; however, a nominally sparser set of activity predicts the aggregate behavior of the population. These findings support an account in which a subset of the neural activity underlying individual choice processes can scale to predict behavioral consistency across people, even when the choice behavior of the sample does not match the aggregate behavior of the population

Accounting for financial instruments in the banking industry: conclusions from a simulation model

The paper analyses the effects of three sets of accounting rules for financial instruments - Old IAS before IAS 39 became effective, Current IAS or US GAAP, and the Full Fair Value (FFV) model proposed by the Joint Working Group (JWG) - on the financial statements of banks. We develop a simulation model that captures the essential characteristics of a modern universal bank with investment banking and commercial banking activities. We run simulations for different strategies (fully hedged, partially hedged) using historical data from periods with rising and falling interest rates. We show that under Old IAS a fully hedged bank can portray its zero economic earnings in its financial statements. As Old IAS offer much discretion, this bank may also present income that is either positive or negative. We further show that because of the restrictive hedge accounting rules, banks cannot adequately portray their best-practice risk management activities under Current IAS or US GAAP. We demonstrate that - contrary to assertions from the banking industry - mandatory FFV accounting adequately reflects the economics of banking activities. Our detailed analysis identifies, in addition, several critical issues of the accounting models that have not been covered in previous literature.

Design of a Thermoelectric Generator for Heavy-Duty Vehicles: Approach Based on WHVC and Real Driving Vehicle Boundary Conditions

Fuel consumption and the CO2 emissions of heavy-duty vehicles are responsible for a great share of the road transport sector and substantial improvements are unlikely without further innovations. One part of this problem is that approximately 2=3 of the fuel’s chemical energy is lost in waste heat through the engine’s coolant and exhaust system. Heavy-duty vehicles are expected to continue using internal combustion engines; a waste heat recovery system provides a future potential to reduce the fuel consumption and the emissions. Thermoelectric Generators offer a low complexity solution. Based on the Seebeck effect, they convert thermal energy directly into electricity. Installed in the exhaust system of a vehicle, the system can supply the vehicles electrical system or charging the battery. Their benefits are low maintenance costs, relatively low system weight, small installation volume, and a competitive cost-benefit ratio. Recent research has focused on passenger cars but the potential for heavy-duty vehicles is high as well. Therefore, in this paper, the system development from potential analysis over design to experimental results, is presented for modern Euro VI heavy-duty vehicles with diesel and natural gas engines. The system integration is considered by analyzing installation positions in the exhaust aftertreatment system and its boundary conditions, such as available installation space and exhaust enthalpies for the most suitable positions. For this purpose, real road driving experimental data from long-haulage road circuit Stuttgart-Hamburg-Stuttgart and the representative World Harmonized Vehicle Cycle are presented as reference. Based on this data an approach for developing a thermoelectric generator system is investigated. The experimentally determined results of a hardware test and a simulation-based potential analysis are given for the vehicle interactions, the expected net electrical output power, and the reduced fuel consumption