Rule Britannia!: British Stock Market Returns, 1825-1870

This article presents a new series of monthly equity returns for the British stock market for the period 1825-1870. In addition to calculating capital appreciation and dividend yields, the article also estimates the effect of survivorship bias on returns. Three notable findings emerge from this study. First, stock market returns in the 1825-1870 period are broadly similar for Britain and the United States, although the British market is less risky. Second, real returns in the 1825-1870 period are higher than in subsequent epochs of British history. Third, unlike the modern era, dividends are the most important component of returns.

'Rule of Law and Politics of Anti-Corruption Campaigns in a Post-Authoritarian State- The Nigerian Case'

This article evaluates the anti-corruption campaign instituted in Nigeria following on the post-authoritarian transition in the country, with specific focus on political corruption. The anti-corruption campaign is being prosecuted within a context where law is as critical a factor as politics. This article examines whether the judiciary, in view of its accountability deficit, can offer legitimacy to the campaign. How has its questionable credentials impacted on its involvement in the campaign to sanitise public life? What has been the impact of the judicial role on the rule of law? These are some of the important questions this article seeks to answer. The inquiry in this article demonstrates how the guardian institution of the rule of law faces an uphill task in the performance of that role in a post-authoritarian context.

A heterosynaptic learning rule for neural networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre-and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean leaxning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

Measuring Inconsistency in a Network Intrusion Detection Rule Set Based on Snort

In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. Finally, we propose a new measure of inconsistency for prioritized knowledge which incorporates the normalized number of atoms in a language involved in inconsistency to provide a deeper inspection of inconsistent formulae. We conclude that such measures are useful for the network intrusion domain assuming that introducing expert knowledge for correlation of rules is feasible.

Bridging Jeffrey's Rule, AGM Revision and Dempster Conditioning in the Theory of Evidence

Belief revision characterizes the process of revising an agent’s beliefs when receiving new evidence. In the field of artificial intelligence, revision strategies have been extensively studied in the context of logic-based formalisms and probability kinematics. However, so far there is not much literature on this topic in evidence theory. In contrast, combination rules proposed so far in the theory of evidence, especially Dempster rule, are symmetric. They rely on a basic assumption, that is, pieces of evidence being combined are considered to be on a par, i.e. play the same role. When one source of evidence is less reliable than another, it is possible to discount it and then a symmetric combination operation
is still used. In the case of revision, the idea is to let prior knowledge of an agent be altered by some input information. The change problem is thus intrinsically asymmetric. Assuming the input information is reliable, it should be retained whilst the prior information should be changed minimally to that effect. To deal with this issue, this paper defines the notion of revision for the theory of evidence in such a way as to bring together probabilistic and logical views. Several revision rules previously proposed are reviewed and we advocate one of them as better corresponding to the idea of revision. It is extended to cope with inconsistency between prior and input information. It reduces to Dempster
rule of combination, just like revision in the sense of Alchourron, Gardenfors, and Makinson (AGM) reduces to expansion, when the input is strongly consistent with the prior belief function. Properties of this revision rule are also investigated and it is shown to generalize Jeffrey’s rule of updating, Dempster rule of conditioning and a form of AGM revision.

Indigenous rights in Chile: National identity and majority group support for multicultural policies

We examine support for policies affecting indigenous ethnic minorities in Chile. Specifically, we examine the role of national group definitions that include the largest indigenous group—the Mapuche—in different ways. Based on questionnaire data from nonindigenous Chilean students (N = 338), we empirically distinguish iconic inclusion, whereby the Mapuche are seen as an important part of Chile's history and identity on the one hand, from egalitarian inclusion, which represents the Mapuche as citizens of equal importance to the nonindigenous majority on the other. Both forms of inclusion positively predict support for indigenous rights, independent of participants' political affiliation, strength of national identification, and social distance. A second study (N = 277) replicates this finding whilst controlling for right-wing authoritarianism, social dominance orientation, blind patriotism, and constructive patriotism. It also finds iconic inclusion to be predictive of a pro-Mapuche position regarding the unrest over the issue of ancestral land in 2009. We conclude that understanding how national identity affects attitudes about minority rights necessitates appreciating the importance of particular meanings of nationality, and not only the strength of identification.

A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

The majority of reported learning methods for Takagi-Sugeno-Kang fuzzy neural models to date mainly focus on the improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behaviour when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, to identify the corresponding consequent models which can be directly explained in terms of system behaviour presents a critical step in fuzzy neural modelling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practised in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. A new Jacobian matrix is thus proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues about the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods.

Perceptions of Gender Group Variability in Majority and Minority Contexts Two Field Studies with Nurses and Police Officers

Two field studies demonstrated that majority and minority size moderate perceived group variability. In Study 1 we found an outgroup homogeneity (OH) effect for female nurses in the majority, but an ingroup homogeneity (IH) effect for a token minority of male nurses. In Study 2 we found similar effects in a different setting - an OH effect for policemen in the majority and an IH effect for policewomen in the minority. Although measures of visibility, status, and, especially, familiarity tended to show the same pattern as perceived variability, there was no evidence that they mediated perceived dispersion. Results are discussed in terms of group size, rather than gender, being moderators of perceived variability, and with reference to Kanter's (1977a, 1977b) theory of group proportions.