Iterated reflection principles over full disquotational truth

Iterated reflection principles have been employed extensively to unfold epistemic commitments that are incurred by accepting a mathematical theory. Recently this has been applied to theories of truth. The idea is to start with a collection of Tarski-biconditionals and arrive by finitely iterated reflection at strong compositional truth theories. In the context of classical logic it is incoherent to adopt an initial truth theory in which A and 'A is true' are inter-derivable. In this article we show how in the context of a weaker logic, which we call Basic De Morgan Logic, we can coherently start with such a fully disquotational truth theory and arrive at a strong compositional truth theory by applying a natural uniform reflection principle a finite number of times

Impacts of climate change of seaports: A survey of knowledge, perceptions, and planning efforts among port administrators

Port authorities from around the world were surveyed to ascertain how administrators feel climate change might impact their operations, what level of change would be problematic, and how they plan to adapt to new conditions. The survey was distributed to 350 major ports through two leading international port organizations, the International Association of Ports and Harbors and the American Association of Port Authorities. (PDF contains 4 pages

Neural Network Modelling of Constrained Spatial Interaction Flows

Fundamental to regional science is the subject of spatial interaction. GeoComputation - a new research paradigm that represents the convergence of the disciplines of computer science, geographic information science, mathematics and statistics - has brought many scholars back to spatial interaction modeling. Neural spatial interaction modeling represents a clear break with traditional methods used for explicating spatial interaction. Neural spatial interaction models are termed neural in the sense that they are based on neurocomputing. They are clearly related to conventional unconstrained spatial interaction models of the gravity type, and under commonly met conditions they can be understood as a special class of general feedforward neural network models with a single hidden layer and sigmoidal transfer functions (Fischer 1998). These models have been used to model journey-to-work flows and telecommunications traffic (Fischer and Gopal 1994, Openshaw 1993). They appear to provide superior levels of performance when compared with unconstrained conventional models. In many practical situations, however, we have - in addition to the spatial interaction data itself - some information about various accounting constraints on the predicted flows. In principle, there are two ways to incorporate accounting constraints in neural spatial interaction modeling. The required constraint properties can be built into the post-processing stage, or they can be built directly into the model structure. While the first way is relatively straightforward, it suffers from the disadvantage of being inefficient. It will also result in a model which does not inherently respect the constraints. Thus we follow the second way. In this paper we present a novel class of neural spatial interaction models that incorporate origin-specific constraints into the model structure using product units rather than summation units at the hidden layer and softmax output units at the output layer. Product unit neural networks are powerful because of their ability to handle higher order combinations of inputs. But parameter estimation by standard techniques such as the gradient descent technique may be difficult. The performance of this novel class of spatial interaction models will be demonstrated by using the Austrian interregional traffic data and the conventional singly constrained spatial interaction model of the gravity type as benchmark. References Fischer M M (1998) Computational neural networks: A new paradigm for spatial analysis Environment and Planning A 30 (10): 1873-1891 Fischer M M, Gopal S (1994) Artificial neural networks: A new approach to modelling interregional telecommunciation flows, Journal of Regional Science 34(4): 503-527 Openshaw S (1993) Modelling spatial interaction using a neural net. In Fischer MM, Nijkamp P (eds) Geographical information systems, spatial modelling, and policy evaluation, pp. 147-164. Springer, Berlin

Theory and Test on the Corporate Governance of Financial Cooperative Systems: Merger vs. Networks

This paper presents a study of the economic organization of systems of financial cooperatives (FC). The first part presents a theoretical framework rooted in principles of transaction cost economics (TCE) that seeks to explain empirical regularities observable in systems of FC worldwide. The second part is an empirical study that compares X-efficiency between members of the Quebec Desjardins movement (DM) and the United States Credit Union system (USCU), the first organized as a tight network of institutions and the second composed largely by independent institutions with few ties. The fundamental proposition is that networks, are a superior form of governance mechanism (over markets and mergers) for relatively wide and relevant ranges of contractual hazard and size of the institutions. Further, that networks provide substitute, hierarchy based, control mechanisms when size of the institution dilutes internal governance mechanisms, discouraging subgoal pursuits and expense preferences by agents, both occurring in large FC. The theory allows us to generate a set of testable hypothesis of which we highlight three: i) For small FC, differences in efficiency will be relatively small, if any. ii) Large institutions should display systematically lower efficiency than similar sized FC members of strategic networks. iii) Networks should display lower variance in the size as well as in performance indicators. Throughout, empirical results are consistent with our central theoretical proposition.Transaction cost economics, financial cooperatives, credit unions, networks, corporate governance, technical efficiency, X-efficiency