An Extension to ML to Handle Bound Variables in Data Structures: Preliminary Report

Most conventional programming languages have direct methods for representing first-order terms (say, via concrete datatypes in ML). If it is necessary to represent structures containing bound variables, such as λ-terms, formulas, types, or proofs, these must first be mapped into first-order terms, and then a significant number of auxiliary procedures must be implemented to manage bound variable names, check for free occurrences, do substitution, test for equality modulo alpha conversion, etc. We shall show how the applicative core of the ML programming language can be enhanced so that λ-terms can be represented more directly and so that the enhanced language, called MLλ, provides a more elegant method of manipulating bound variables within data structures. In fact, the names of bound variables will not be accessible to the MLλ programmer. This extension to ML involves the following: introduction of the new type constructor \u27a =\u3e \u27b for the type of λ-terms formed by abstracting a parameter of type \u27a out of a term of type \u27b; a very restricted and simple form of higher-order pattern matching; a method for extending a given data structure with a new constructor; and, a method for extending function definitions to handle such new constructors. We present several examples of MLλ programs

Transferable Control

In this paper, we introduce the notion of transferable control, defined as a situation where one party (the principal, say) can transfer control to another party (the agent) but cannot commit herself to do so. One theoretical foundation for this notion builds on the distinction between formal and real authority introduced by Aghion and Tirole, in which the actual exercise of authority may require noncontractible information, absent which formal control rights are vacuous. We use this notion to study the extent to which control transfers may allow an agent to reveal information regarding his ability or willingness to cooperate with the principal in the future. We show that the distinction between contractible and transferable control can drastically influence how learning takes place: with contractible control, information about the agent can often be acquired through revelation mechanisms that involve communication and message-contingent control allocations; in contrast, when control is transferable but not contractible, it can be optimal to transfer control unconditionally and learn instead from the way in which the agent exercises control.Economic

The involutions-as-principal types/ application-as-unification analogy

In 2005, S. Abramsky introduced various universal models of computation based on Affine Combinatory Logic, consisting of partial involutions over a suitable formal language of moves, in order to discuss reversible computation in a game-theoretic setting. We investigate Abramsky\u2019s models from the point of view of the model theory of \u3bb-calculus, focusing on the purely linear and affine fragments of Abramsky\u2019s Combinatory Algebras. Our approach stems from realizing a structural analogy, which had not been hitherto pointed out in the literature, between the partial involution interpreting a combinator and the principal type of that term, with respect to a simple types discipline for \u3bb-calculus. This analogy allows for explaining as unification between principal types the somewhat awkward linear application of involutions arising from Geometry of Interaction (GoI). Our approach provides immediately an answer to the open problem, raised by Abramsky, of characterising those finitely describable partial involutions which are denotations of combinators, in the purely affine fragment. We prove also that the (purely) linear combinatory algebra of partial involutions is a (purely) linear \u3bb-algebra, albeit not a combinatory model, while the (purely) affine combinatory algebra is not. In order to check the complex equations involved in the definition of affine \u3bb-algebra, we implement in Erlang the compilation of \u3bb-terms as involutions, and their execution