Constitutive modelling of Sandvik 1RK91

A physically based constitutive equation is being developed for the maraging\ud stainless steel Sandvik 1RK91. The steel is used to make precision parts. These parts are formed through multistage forming operations and heat treatments from cold rolled and annealed sheets. The specific alloy is designed to be thermodynamically unstable, so that deformation even at room temperatures can bring about a change in the phase of face centred cubic austenite to either hexagonal closed packed martensite and/or, body centred cubic martensite. This solid state phase change is a function of the strain path, strain, strain rate and temperature. Thus, the fraction of the new phase formed depends on the state of stress at a given location in the part being formed. Therefore a set of experiments is being conducted in order to quantify the stress-strain behavior of this steel under various stress states, strain, strain rate as well as temperature. A magnetic sensor records the fraction of ferromagnetic martensite formed from paramagnetic austenite. A thermocouple as well as an infra red thermometer is used to log the change in temperature of the steel during a mechanical test. The force-displacement data are converted to stress-strain data after correcting for the changes in strain rate and temperature. These data are then cast into a general form of constitutive equation and the transformation equations are derived from Olson-Cohen type functions

Overcoming cross-cultural group work tensions: mixed student perspectives on the role of social relationships

As universities worldwide rapidly internationalise, higher education classrooms have become unique spaces for collaboration between students from different countries. One common way to encourage collaboration between diverse peers is through group work. However, previous research has highlighted that cross-cultural group work can be challenging and has hinted at potential social tensions. To understand this notion better, we have used robust quantitative tools in this study to select 20 participants from a larger classroom of 860 students to take part in an in-depth qualitative interview about cross-cultural group work experiences. Participant views on social tensions in cross-cultural group work were elicited using a unique mediating artefact method to encourage reflection and in-depth discussion. In our analysis of emergent interview themes, we compared student perspectives on the role of social relationships in group work by their academic performance level. Our findings indicated that all students interviewed desired the opportunity to form social relationships with their group work members, but their motivations for doing so varied widely by academic performance level

Heterogeneous Agent Models: Two Simple Case Studies

These notes review two simple heterogeneous agent models in economics and finance. The first is a cobweb model with rational versus naive agents introduced in Brock and Hommes (1997). The second is an asset pricing model with fundamentalists versus technical traders introduced in Brock and Hommes (1998). Agents are boundedly rational and switch between different trading strategies, based upon an evolutionary fitness measure given by realized past profits. Evolutionary switching creates a nonlinearity in the dynamics. Rational routes to randomness, that is, bifurcation routes to complicated dynamical behaviour occur when agents become more sensitive to differences in evolutionary fitness

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

U.S. stock market interaction network as learned by the Boltzmann Machine

We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented analysis shows that binarization preserves market correlation structure. Properties of distributions of external fields and couplings as well as industry sector clustering structure are studied for different historical dates and moving window sizes. We found that a heavy positive tail in the distribution of couplings is responsible for the sparse market clustering structure. We also show that discrepancies between the model parameters might be used as a precursor of financial instabilities.Comment: 15 pages, 17 figures, 1 tabl

As If or What? - Expectations and Optimization in a Simple Macroeconomic Environment

In this paper we report the results of a laboratory experiment, in which we observed the behavior of agents in a simple macroeconomic setting. The structure of the economy was only partially known to the players which is a realistic feature of our experiment. We investigate whether subjects manage to approach optimal behavior even if they lack important information. Furthermore, we analyze subjects' perceptions of the model and whether their behavior is consistent with their perceptions. The full information model predicts changes of employment correctly, but not the level of employment. In the aggregate, subjects have correct perceptions, although individual perceptions are biased. We finally show that deviations from the full information solution are due to optimization failures than than misperceptions

Bubble Formation and (In)Efficient Markets in Learning-to-Forecast and -Optimise Experiments

This experiment compares the price dynamics and bubble formation in an asset market with a price adjustment rule in three treatments where subjects (1) submit a price forecast only, (2) choose quantity to buy/sell and (3) perform both tasks. We find deviation of the market price from the fundamental price in all treatments, but to a larger degree in treatments (2) and (3). Mispricing is therefore a robust finding in markets with positive expectation feedback. Some very large, recurring bubbles arise, where the price is 3 times larger than the fundamental value, which were not seen in former experiments

The National Dutch Breast Implant Registry: user-reported experiences and importance

Background: Robust (inter-)national breast implant registries are important. For some, registries are an administrative burden, for others they represent a solution for the discussions involving breast implants. The DBIR is one of the first national, opt-out, clinical registries of breast implants, providing information for clinical auditing and product recall. Four years after its introduction, it is time to address users’ comments in order to keep improving quality of registration, and patient safety. This study assesses users’ feedback focusing on importance of registration, logistics and user experience, and areas of improvement. Methods: In May 2018, a standardized online study–specific questionnaire was sent out to all members of the Netherlands Society of Plastic Surgery. Descriptive statistics were reported in absolute frequencies and/or percentages. Results: A total of 102 members responded to the questionnaire (response rate, 24.2%). Of all respondents, 97.1% were actively registering in DBIR. Respondents rated the importance of registration in DBIR as 8.1 out of 10 points. Ninety-one respondents suggested improvements for the DBIR. All comments were related to registration convenience and provision of automatically generated data. Conclusions: Respondents believe that registration is highly important and worth the administrative burden. However, we should collectively keep improving accuracy, usability and sustainability of breast

Prospect Theory in the Heterogeneous Agent Model

Using the Heterogeneous Agent Model framework, we incorporate an extension based on Prospect Theory into a popular agent-based asset pricing model. The extension covers the phenomenon of loss aversion manifested in risk aversion and asymmetric treatment of gains and losses. Using Monte Carlo methods, we investigate behavior and statistical properties of the extended model and assess its relevance with respect to financial data and stylized facts. We show that the Prospect Theory extension keeps the essential underlying mechanics of the model intact, however, that it changes the model dynamics considerably. Stability of the model increases but the occurrence of the fundamental strategy is more extreme. Moreover, the extension shifts the model closer to the behavior of real-world stock markets