Adaptivity in programming languages

A programming style can be seen as a particular model of shaping thought or a special way of codifying language to solve a problem. An adaptive device is made up of an underlying formalism, for instance, an automaton, a grammar, a decision tree, etc., and an adaptive mechanism, responsible for providing features for self-modification. Adaptive languages are obtained by using some programming language as the device’s underlying formalism. The conception of such languages calls for a new programming style, since the application of adaptive technology in the field of programming languages suggests a new way of thinking. Adaptive languages have the basic feature of allowing the expression of programs which self-modifying through adaptive actions at runtime. With the adaptive style, programming language codes can be structured in such a way that the codified program therein modifies or adapts itself towards the needs of the problem. The adaptive programming style may be a feasible alternate way to obtain self-modifying consistent codes, which allow its use in modern applications for self-modifying code.

Adaptive device with underlying mechanism defined by a programming language

An adaptive device is made up of an underlying mechanism, for instance, an automaton, a grammar, a decision tree, etc., to which is added an adaptive mechanism, responsible for allowing a dynamic modification in the structure of the underlying mechanism. This article aims to investigate if a programming language can be used as an underlying mechanism of an adaptive device, resulting in an adaptive language.

Conception of adaptive programming languages

Adaptive devices show the characteristic of dynamically change themselves in response to input stimuli with no interference of external agents. Occasional changes in behaviour are immediately detected by the devices, which right away react spontaneously to them. Chronologically such devices derived from researches in the field of formal languages and automata. However, formalism spurred applications in several other fields. Based on the operation of adaptive automata, the elementary ideas generanting programming adaptive languages are presented.

Adaptive languages and a new programming style

A programming style can be seen as a particular model of shaping thought or a special way of codifying language to solve a problem. Adaptive languages have the basic feature of allowing the expression of programs which self-modifying through adaptive actions at runtime. The conception of such languages calls for a new programming style, since the application of adaptive technology in the field of programming languages suggests a new way of thinking. With the adaptive style, programming language codes can be structured in such a way that the codified program therein modifies or adapts itself towards the needs of the problem. The adaptive programming style may be a feasible alternate way to obtain self-modifying consistent codes, which allow its use in modern applications for self-modifying code.

Using adaptive automata in a multi-paradigm programming environment

In this paper the architecture of an experimental multiparadigmatic programming environment is sketched, showing how its parts combine together with application modules in order to perform the integration of program modules written in different programming languages and paradigms. Adaptive automata are special self-modifying formal state machines used as a design and implementation tool in the representation of complex systems. Adaptive automata have been proven to have the same formal power as Turing Machines. Therefore, at least in theory, arbitrarily complex systems may be modeled with adaptive automata. The present work briefly introduces such formal tool and presents case studies showing how to use them in two very different situations: the first one, in the name management module of a multi-paradigmatic and multi-language programming environment, and the second one, in an application program implementing an adaptive automaton that accepts a context-sensitive language.

Why do bidders drop out from a sequential auction

In actual sequential auctions, 1) bidders typically incur a cost in continuing from one sale to the next, and 2) bidders decide whether or not to continue. To investigate the question "why do bidders drop out," we define a sequential auction model with continuation costs and an endogenously determined number of bidders at each sale, and we characterize the equilibria in this model. Simple examples illustrate the effect of several possible changes to this model.

Synergies and price trends in sequential auctions

In this paper we consider sequential auctions where an individual’s value for a bundle of objects is either greater than the sum of the values for the objects separately (positive synergy) or less than the sum (negative synergy). We show that the existence of positive synergies implies declining expected prices. When synergies are negative, expected prices are increasing. There are several corollaries. First, the seller is indi¤erent between selling the objects simultaneously as a bundle or sequentially when synergies are positive. Second, when synergies are negative, the expected revenue generated by the simultaneous auction can be larger or smaller than the expected revenue generated by the sequential auction. In addition, in the presence of positive synergies, an option to buy the additional object at the price of the …rst object is never exercised in the symmetric equilibrium and the seller’s revenue is unchanged. Under negative synergies, in contrast, if there is an equilibrium where the option is never exercised, then equilibrium prices may either increase or decrease and, therefore, the net e¤ect on the seller’s revenue of the introduction of an option is ambiguous. Finally, we examine two special cases with asymmetric players. In the …rst case, players have distinct synergies. In this example, even if one player has positive synergies and the other has negative synergies, it is still possible for expected prices to decline. In the second case, one player wants two objects and the remaining players want one object each. For this example, we show that expected prices may not necessarily decrease as predicted by Branco (1997). The reason is that players with singleunit demand will generally bid less than their true valuations in the …rst period. Therefore, there are two opposing forces; the reduction in the bid of the player with multiple-demand in the last auction and less aggressive bidding in the …rst auction by the players with single-unit demand.