Evidence for surprise minimization over value maximization in choice behavior

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus 'keep their options open'. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations

Robustness and Generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work

How market structure drives commodity prices

We introduce an agent-based model, in which agents set their prices to maximize profit. At steady state the market self-organizes into three groups: excess producers, consumers and balanced agents, with prices determined by their own resource level and a couple of macroscopic parameters that emerge naturally from the analysis, akin to mean-field parameters in statistical mechanics. When resources are scarce prices rise sharply below a turning point that marks the disappearance of excess producers. To compare the model with real empirical data, we study the relationship between commodity prices and stock-to-use ratios in a range of commodities such as agricultural products and metals. By introducing an elasticity parameter to mitigate noise and long-term changes in commodities data, we confirm the trend of rising prices, provide evidence for turning points, and indicate yield points for less essential commodities

Hall Measurements on Carbon Nanotube Paper Modified With Electroless Deposited Platinum

Carbon nanotube paper, sometimes referred to as bucky paper, is a random arrangement of carbon nanotubes meshed into a single robust structure, which can be manipulated with relative ease. Multi-walled carbon nanotubes were used to make the nanotube paper, and were subsequently modified with platinum using an electroless deposition method based on substrate enhanced electroless deposition. This involves the use of a sacrificial metal substrate that undergoes electro-dissolution while the platinum metal deposits out of solution onto the nanotube paper via a galvanic displacement reaction. The samples were characterized using SEM/EDS, and Hall-effect measurements. The SEM/EDS analysis clearly revealed deposits of platinum (Pt) distributed over the nanotube paper surface, and the qualitative elemental analysis revealed co-deposition of other elements from the metal substrates used. When stainless steel was used as sacrificial metal a large degree of Pt contamination with various other metals was observed. Whereas when pure sacrificial metals were used bimetallic Pt clusters resulted. The co-deposition of a bimetallic system upon carbon nanotubes was a function of the metal type and the time of exposure. Hall-effect measurements revealed some interesting fluctuations in sheet carrier density and the dominant carrier switched from N- to P-type when Pt was deposited onto the nanotube paper. Perspectives on the use of the nanotube paper as a replacement to traditional carbon cloth in water electrolysis systems are also discussed

Organic nanofibers embedding stimuli-responsive threaded molecular components

While most of the studies on molecular machines have been performed in solution, interfacing these supramolecular systems with solid-state nanostructures and materials is very important in view of their utilization in sensing components working by chemical and photonic actuation. Host polymeric materials, and particularly polymer nanofibers, enable the manipulation of the functional molecules constituting molecular machines, and provide a way to induce and control the supramolecular organization. Here, we present electrospun nanocomposites embedding a self-assembling rotaxane-type system that is responsive to both optical (UV-visible light) and chemical (acid/base) stimuli. The system includes a molecular axle comprised of a dibenzylammonium recognition site and two azobenzene end groups, and a dibenzo[24]crown-8 molecular ring. The dethreading and rethreading of the molecular components in nanofibers induced by exposure to base and acid vapors, as well as the photoisomerization of the azobenzene end groups, occur in a similar manner to what observed in solution. Importantly, however, the nanoscale mechanical function following external chemical stimuli induces a measurable variation of the macroscopic mechanical properties of nanofibers aligned in arrays, whose Young's modulus is significantly enhanced upon dethreading of the axles from the rings. These composite nanosystems show therefore great potential for application in chemical sensors, photonic actuators and environmentally responsive materials.Comment: 39 pages, 16 figure

Altered Risk-Based Decision Making following Adolescent Alcohol Use Results from an Imbalance in Reinforcement Learning in Rats

Alcohol use during adolescence has profound and enduring consequences on decision-making under risk. However, the fundamental psychological processes underlying these changes are unknown. Here, we show that alcohol use produces over-fast learning for better-than-expected, but not worse-than-expected, outcomes without altering subjective reward valuation. We constructed a simple reinforcement learning model to simulate altered decision making using behavioral parameters extracted from rats with a history of adolescent alcohol use. Remarkably, the learning imbalance alone was sufficient to simulate the divergence in choice behavior observed between these groups of animals. These findings identify a selective alteration in reinforcement learning following adolescent alcohol use that can account for a robust change in risk-based decision making persisting into later life