Testing Dividend Signalling Models

This paper derives a key monotonicity property common to all dividend signaling models: the greater the rate that dividend income is taxed relative to capital gains income, the greater the value of information revealed by a given dividend, and hence the greater the associated excess return. This monotonicity condition is tested with robust non-parametric techniques. No evidence is found to support dividend signaling models. The same results are inconsistent with tax-based CAPM arguments

An Operational Interpretation of Negative Probabilities and No-Signalling Models

Negative probabilities have long been discussed in connection with the foundations of quantum mechanics. We have recently shown that, if signed measures are allowed on the hidden variables, the class of probability models which can be captured by local hidden-variable models are exactly the no-signalling models. However, the question remains of how negative probabilities are to be interpreted. In this paper, we present an operational interpretation of negative probabilities as arising from standard probabilities on signed events. This leads, by virtue of our previous result, to a systematic scheme for simulating arbitrary no-signalling models.Comment: 13 pages, 2 figure

A proper test of overconfidence

In this paper we conduct two proper tests of overconfidence. We reject the hypothesis "the data cannot be generated by a rational model" in both experiments.Overconfidence; Better than Average; Experimental Economics; Irrationality; Signalling Models

Does the Better-Than-Average Effect Show That People Are Overconfident?: An Experiment.

We conduct a proper test of the claim that people are overconfident, in the sense that they believe that they are better than others. The results of the experiment we present do not allow us to reject the hypotheses that the data has been generated by perfectly rational, unbiased, and appropriately confident agents.Overconfidence; Better than Average; Experimental Economics; Irrationality; Signalling Models

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Overconfidence?

Many studies have shown that people display an apparent overconfidence. In particular, it is common for a majority of people to describe themselves as better-than-average. The literature takes for granted that this better-than-average is problematic. We argue, however, that, even accepting these studies on their own terms, there is nothing at all wrong with a strict majority of people rating themselves above the median.Overconfidence; Irrationality; Signalling Models; Better than average

Overconfidence?

Many studies have shown that people display an apparent overconfidence. In particular, it is common for a majority of people to describe themselves as better than average. The literature takes for granted that this better-than-average effect is problematic. We argue, however, that, even accepting these studies completely on their own terms, there is nothing at all wrong with a strict majority of people rating themselves above the median.Overconfidence; Better than Average; Experiments; Irrationality; Signalling Models

Overconfidence?

Many studies have shown that people display an apparent overconfidence. In particular, it is common for a majority of people to describe themselves as better than average. The literature takes for granted that this better-than-average effect is problematic. We argue, however, that, even accepting these studies completely on their own terms, there is nothing at all wrong with a strict majority of people rating themselves above the median.Overconfidence; Better than Average; Experimental Economics; Irrationality; Signalling Models

Overconfidence?

Many studies have shown that people display an apparent overconfidence. In particular, it is common for a majority of people to describe themselves as better-than-average. The literature takes for granted that this better-than-average effect is problematic. We argue, however, that, even accepting these studies on their own terms, there is nothing at all wrong with a strict majority of people rating themselves above the median.Overconfidence, Better than Average, Experiments, Irrationality, Signalling Models