The Value of Private Information within Evolving Groups

This note offers an analytical framework aimed at explaining how individual agents purposefully act with the goal of managing the value of their information sets. Agents undertake a process of private accumulation of information, which takes into account the non-rival nature of this peculiar entity. Non rivalry introduces an externality that might trigger long-term endogenous fluctuations. The dynamics of interaction, namely the possibility of entering or exiting the group to which the individuals belong, wil l determine time trajectories for the information flows that are unique for the specific conditions of interaction that are being considered at a given momentEste artigo apresenta uma estrutura analítica que tem por objetivo explicar como é que os agentes individuais atuam, de modo intencional, com o propósito de gerir o valor da informação que detêm. Os agentes prosseguem um processo de acumulação privada de informação, o qual toma em consideração a natureza não rival desta entidade que detém características específicas. A não rivalidade introduz uma externalidade que pode despoletar flutuações endógenas de longo prazo. A dinâmica de interação, nomeadamente a possibilidade de entrar ou sair do grupo a que os indivíduos pertencem, vai determinar a formação de trajetórias no tempo para os fluxos de informação, as quais são únicas para as condições particulares de interação que estão a ser consideradas num determinado momento.

One-sided tolerance interval in a two-way balanced nested model with mixed effects

In many research areas (such as public health, environmental contamination, and others) one deals with the necessity of using data to infer whether some proportion (%) of a population of interest is (or one wants it to be) below and/or over some threshold, through the computation of tolerance interval. The idea is, once a threshold is given, one computes the tolerance interval or limit (which might be one or two - sided bounded) and then to check if it satisfies the given threshold. Since in this work we deal with the computation of one - sided tolerance interval, for the two-sided case we recomend, for instance, Krishnamoorthy and Mathew [5]. Krishnamoorthy and Mathew [4] performed the computation of upper tolerance limit in balanced and unbalanced one-way random effects models, whereas Fonseca et al [3] performed it based in a similar ideas but in a tow-way nested mixed or random effects model. In case of random effects model, Fonseca et al [3] performed the computation of such interval only for the balanced data, whereas in the mixed effects case they dit it only for the unbalanced data. For the computation of twosided tolerance interval in models with mixed and/or random effects we recomend, for instance, Sharma and Mathew [7]. The purpose of this paper is the computation of upper and lower tolerance interval in a two-way nested mixed effects models in balanced data. For the case of unbalanced data, as mentioned above, Fonseca et al [3] have already computed upper tolerance interval. Hence, using the notions persented in Fonseca et al [3] and Krishnamoorthy and Mathew [4], we present some results on the construction of one-sided tolerance interval for the balanced case. Thus, in order to do so at first instance we perform the construction for the upper case, and then the construction for the lower case.