The effects of group adaptation on task performance: An agent-based approach.

Organisations rely upon group formation to solve complex tasks, and groups often adapt to the demands of the task they face by changing their composition periodically. Previous research has often employed experimental, survey-based, and fieldwork methods to study the effects of group adaptation on task performance. This paper, by contrast, employs an agent-based approach to study these effects. There are three reasons why we do so. First, agent-based modelling and simulation allows to take into account further factors that might moderate the relationship between group adaptation and task performance, such as individual learning and task complexity. Second, such an approach allows to study large variations in the variables of interest, which contributes to the generalisation of our results. Finally, by employing an agent-based approach, we are able to study the longitudinal effects of group adaptation on task performance. Longitudinal analyses are often missing in prior related research. Our results indicate that reorganising well-performing groups might be beneficial, but only if individual learning is restricted. However, there are also cases in which group adaptation might unfold adverse effects. We provide extensive analyses that shed additional light on and help explain the ambiguous results of previous research.

Understanding the dynamics emerging from infodemics: a call to action for interdisciplinary research.

Research on infodemics, i.e., the rapid spread of (mis)information related to a hazardous event, such as the COVID-19 pandemic, requires integrating a multiplicity of scientific disciplines. The dynamics emerging from infodemics have the potential to generate complex behavioral patterns. To react appropriately, it is of ultimate importance for the fields of Business and Economics to understand these dynamics. In the short run, they might lead to an adaptation in household spending or to a shift in buying behavior towards online providers. In the long run, changes in investments, consumer behavior, and markets are to be expected. We argue that the dynamics emerge from complex interactions among multiple factors, such as information and misinformation accessible to individuals and the formation and revision of beliefs. (Mis)information accessible to individuals is, amongst others, affected by algorithms specifically designed to provide personalized information, while automated fact-checking algorithms can help reduce the amount of circulating misinformation. The formation and revision of individual (and probably false) beliefs and individual fact-checking and interpretation of information are heavily affected by linguistic patterns inherent to information during pandemics and infodemics and further factors, such as affect, intuition, and motives. We argue that, to get a deep(er) understanding of the dynamics emerging from infodemics, the fields of Business and Economics should integrate the perspectives of Computer Science and Information Systems, (Computational) Linguistics, and Cognitive Science into the wider context of economic systems (e.g., organizations, markets or industries) and propose a way to do so. As research on infodemics is a strongly interdisciplinary field and the integration of the above-mentioned disciplines is a first step towards a holistic approach, we conclude with a call to action which should encourage researchers to collaborate across scientific disciplines and unfold collective creativity, which will substantially advance research on infodemics.

Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities.

In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments' efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that-in some setups-a certain extent of misforecasting is desirable from the firm's point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that-in particular for relatively good forecasters-most of our results are robust to changes in setting the parameters of our multi-agent simulation model.

Multiobjective decision making policies and coordination mechanisms in hierarchical organizations: results of an agent-based simulation.

This paper analyses how different coordination modes and different multiobjective decision making approaches interfere with each other in hierarchical organizations. The investigation is based on an agent-based simulation. We apply a modified NK-model in which we map multiobjective decision making as adaptive walk on multiple performance landscapes, whereby each landscape represents one objective. We find that the impact of the coordination mode on the performance and the speed of performance improvement is critically affected by the selected multiobjective decision making approach. In certain setups, the performances achieved with the more complex multiobjective decision making approaches turn out to be less sensitive to the coordination mode than the performances achieved with the less complex multiobjective decision making approaches. Furthermore, we present results on the impact of the nature of interactions among decisions on the achieved performance in multiobjective setups. Our results give guidance on how to control the performance contribution of objectives to overall performance and answer the question how effective certain multiobjective decision making approaches perform under certain circumstances (coordination mode and interdependencies among decisions).

Oncolytic adenoviral mutants with E1B19K gene deletions enhance gemcitabine-induced apoptosis in pancreatic carcinoma cells and anti-tumor efficacy in vivo.

PURPOSE: Pancreatic adenocarcinoma is a rapidly progressive malignancy that is highly resistant to current chemotherapeutic modalities and almost uniformly fatal. We show that a novel targeting strategy combining oncolytic adenoviral mutants with the standard cytotoxic treatment, gemcitabine, can markedly improve the anticancer potency. EXPERIMENTAL DESIGN: Adenoviral mutants with the E1B19K gene deleted with and without E3B gene expression (AdDeltaE1B19K and dl337 mutants, respectively) were assessed for synergistic interactions in combination with gemcitabine. Cell viability, mechanism of cell death, and antitumor efficacy in vivo were determined in the pancreatic carcinoma cells PT45 and Suit2, normal human bronchial epithelial cells, and in PT45 xenografts. RESULTS: The DeltaE1B19K-deleted mutants synergized with gemcitabine to selectively kill cultured pancreatic cancer cells and xenografts in vivo with no effect in normal cells. The corresponding wild-type virus (Ad5) stimulated drug-induced cell killing to a lesser degree. Gemcitabine blocked replication of all viruses despite the enhanced cell killing activity due to gemcitabine-induced delay in G1/S-cell cycle progression, with repression of cyclin E and cdc25A, which was not abrogated by viral E1A-expression. Synergistic cell death occurred through enhancement of gemcitabine-induced apoptosis in the presence of both AdDeltaE1B19K and dl337 mutants, shown by increased cell membrane fragmentation, caspase-3 activation, and mitochondrial dysfunction. CONCLUSIONS: Our data suggest that oncolytic mutants lacking the antiapoptotic E1B19K gene can improve efficacy of DNA-damaging drugs such as gemcitabine through convergence on cellular apoptosis pathways. These findings imply that less toxic doses than currently practiced in the clinic could efficiently target pancreatic adenocarcinomas when combined with adenoviral mutants.

Physarum nitric oxide synthases: genomic structures and enzymology of recombinant proteins.

Physarum polycephalum expresses two closely related, calcium-independent NOSs (nitric oxide synthases). In our previous work, we showed that both NOSs are induced during starvation and apparently play a functional role in sporulation. In the present study, we characterized the genomic structures of both Physarum NOSs, expressed both enzymes recombinantly in bacteria and characterized their biochemical properties. Whereas the overall genomic organization of Physarum NOS genes is comparable with various animal NOSs, none of the exon-intron boundaries are conserved. Recombinant expression of clones with various N-termini identified N-terminal amino acids essential for enzyme activity, but not required for haem binding or dimerization, and suggests the usage of non-AUG start codons for Physarum NOSs. Biochemical characterization of the two Physarum isoenzymes revealed different affinities for L-arginine, FMN and 6R-5,6,7,8-tetrahydro-L-biopterin.