Transformations of the commodity space, behavioral heterogeneity, and the aggregation problem

The dynamics of social networks can determine the transmission of information, the spread of diseases, and the evolution of behavior. Despite this broad importance, a general framework for predicting social network stability has not been proposed. Here we present longitudinal data on the social dynamics of a cooperative bird species, the wire-tailed manakin, to evaluate the potential causes of temporal network stability. We find that when partners interact less frequently and when social connectedness increases, the network is subsequently less stable. Social connectivity was also negatively associated with the temporal persistence of coalition partnerships on an annual timescale. This negative association between connectivity and stability was surprising, especially given that individual manakins who were more connected also had more stable partnerships. This apparent paradox arises from a within-individual behavioral trade-off between partnership quantity and quality. Crucially, this trade-off is easily masked by behavioral variation among individuals. Using a simulation, we show that these results are explained by a simple model that combines among-individual behavioral heterogeneity and reciprocity within the network. As social networks become more connected, individuals face a trade-off between partnership quantity and maintenance. This model also demonstrates how among-individual behavioral heterogeneity, a ubiquitous feature of natural societies, can improve social stability. Together, these findings provide unifying principles that are expected to govern diverse social systems.

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A Novel Solution for the Aggregation Problem in Natural Language Interface to Databases (NLIDB)

10.5753/sbbd.2020.13644 ◽

2020 ◽

Author(s):

Alexandre Ferreira Novello ◽

Marco Antonio Casanova

Keyword(s):

Natural Language ◽

Basic Unit ◽

Aggregation Functions ◽

Natural Language Interface ◽

Aggregation Problem ◽

The Creation ◽

Multiple Attributes ◽

Generic Module

Natural Language Interface to Databases (NLIDB) systems usually do not deal with aggregations, which can be of two types: aggregation functions (such as count, sum, average, minimum, and maximum) and grouping functions (GROUP BY). This paper addresses the creation of a generic module, to be used in NLIDB systems, that allows such systems to perform queries with aggregations, on the condition that the query results the NLIDB returns are or can be transformed into tables. The paper covers aggregations with specificities, such as ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic unit measure recognition, and aggregations in attributes with compound names.

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