Resilience as Anticipation in Organizational Systems: An Agent-based Computational Approach.

The literature on organizational resilience explores various viewpoints, ranging from strategies to recover after disruptions to proactive anticipation of threats. Formal models primarily focus on the ability to recover from shocks, analyzing factors like deviation from performance targets, recovery time, and potential adaptation in function and structure. However, incorporating anticipation into such models remains scarce. Additionally, existing anticipatory systems models often neglect key aspects of organizational behavior. This work addresses these gaps by introducing an agent-based modeling approach that integrates anticipation into organizational decision-making. Our computational model features agents embedded in different organizational structures who make decisions based on projected market states (levels and trends). These decisions are subject to delays in perceiving market conditions and vary depending on the organization's adaptive capacity to update its offering. We analyze different organizational structures and market behaviors (trend direction and volatility). Our results indicate that full connectivity among agents can be detrimental to organizational resilience, as it may reduce the diversity of anticipation strategies for forecasting the market. Conversely, either sparse or highly clustered networks demonstrate a greater ability, on average, to keep up with changing market levels and trends.

Varying the hydrophobicity of humic matter by a phase-transfer-catalyzed O-alkylation reaction.

An O-alkylation reaction catalyzed by tetrabutylammonium hydroxide (TBAH) as a phase-transfer agent was applied to a humic acid (HA) to modify its hydrophobic properties. The carboxyl and hydroxyl functional groups of HA acted as nucleophiles in substitution reactions (Sn2) with methyl iodide, pentyl bromide and benzyl bromide added in amounts equimolar to 20, 60 and 80% of HA total nucleophilic sites. The occurrence of O-alkylation was shown by DRIFT spectrometry, NMR spectroscopy, High Performance Size Exclusion Chromatography (HPSEC) and elemental analysis of reaction products. DRIFT spectra showed changes in C-H stretching and bending regions following the insertion of methyl and pentyl groups, while the incorporation of benzyl groups revealed the characteristics aromatic C-H stretching bands. Both liquid- and solid-state NMR spectra revealed characteristic signals for alkyl/aryl esters and ethers. HPSEC chromatograms of alkylated materials invariably displayed an increase in hydrodynamic volume in respect to the original HA, thereby suggesting that the enhanced hydrophobicity conveyed further associations among humic molecules. Analytical, HPSEC and spectroscopic results suggest that benzylation was the most effective reaction at all percentages of HA total nucleophilicity, followed, in the order, by pentylation and methylation, The benzylation reaction was used to improve reaction and work-up conditions and show that HA could be efficiently alkylated also with substantial reduction of TBAH amount, with no THF addition, increase of reaction time and of washing cycles to remove catalyst impurities. These findings indicate that the hydrophobicity of humic substances can be modulated through a mild O-alkylation reaction under a phase-transfer catalysis according to the extent of exposed HA nucleophilic sites. Such a structural modification of humic matter may have multiple chemical, environmental and biological applications.

Computational Modeling Approaches to OrganizationalChange.

Change is ubiquitous in the study of organizations. Organizational change is characterized by multiple perspectives, both conceptually and methodologically. Computational modeling efforts are not the exception. In this work, we aim to provide an analysis of computational modeling approaches to organizational change. For that, we first review published works that directly connect to developing knowledge in organizational change from a computational lens. Second, we offer an account of unexplored topics in computational organizational change. Last, we highlight the potentialities of computer simulation models based on agent interactions in regard to how they could contribute to the understanding of central issues in this organizational research subfield.

The coevolution of the firm and the product attribute space.

Traditionally, firm competition has been studied in contexts where the dimensionality of the product attribute space is given, and firms deploy their strategies constrained by this space. However, firms may exert influence on the local structure of the product attribute space by offering product variants with new attributes. As a result, the geometry of the product attribute space would change endogenously through firms' actions, and this emergent new geometry modifies the conditions for subsequent firm behavior. By focusing on this interplay between actors and conditions, we explore the co-evolution of the firm and the product attribute space. Through a multi-variant Cournot competition framework, we develop a computational model in which firms invest to differentiate their products from other variants, but as minimally as possible so that demand from closely similar existing variants can be stolen. We introduce the fraction dimensionality of the attribute space as our critical independent variable, to reflect saturation of the space with product varieties. The simulation reveals that while new product variants are typically introduced by firms with scale economies, their performance gap with firms without scale economies reduces as fraction dimensionality increases. This indicates that space geometry evolution may favor small-scale players, even when their large-scale competitors are the driving force behind attribute space changes.

The Influence of Homophilous Interactions on Diversity Effects in Group Problem-Solving.

Increasingly diversity researchers call for further studies of group micro-processes and dynamics to understand the paradoxical effects of diversity on group performance. In this study, based on analyses of in-group, networked, homophilous interactions, we aim to explain further the effects of diversity on group performance in a parallel problem-solving task, both experimentally and computationally. We developed a 'whodunit' problem-solving experiment with 116 participants assigned to different-sized groups. Experimental results show that low diversity and high homophily levels are associated with lower performance while the effects of group size are not significant. To investigate this further, we developed an agent-based computational model (ABM), through which we inspected (a) the effect of different homophily and diversity strengths on performance, and (b) the robustness of such effects across group size variations. Overall, modeling results were consistent with our experimental findings, and revealed that the strength of homophily can drive diversity towards a positive or negative impact on performance. We also observed that increasing group size has a very marginal effect. Our work contributes to a better understanding of the implications of diversity in-group problem-solving by providing an integration of both experimental and computational perspectives in the analysis of group processes.

Micro-Level Adaptation, Macro-Level Selection, and the Dynamics of Market Partitioning.

This paper provides a micro-foundation for dual market structure formation through partitioning processes in marketplaces by developing a computational model of interacting economic agents. We propose an agent-based modeling approach, where firms are adaptive and profit-seeking agents entering into and exiting from the market according to their (lack of) profitability. Our firms are characterized by large and small sunk costs, respectively. They locate their offerings along a unimodal demand distribution over a one-dimensional product variety, with the distribution peak constituting the center and the tails standing for the peripheries. We found that large firms may first advance toward the most abundant demand spot, the market center, and release peripheral positions as predicted by extant dual market explanations. However, we also observed that large firms may then move back toward the market fringes to reduce competitive niche overlap in the center, triggering nonlinear resource occupation behavior. Novel results indicate that resource release dynamics depend on firm-level adaptive capabilities, and that a minimum scale of production for low sunk cost firms is key to the formation of the dual structure.

Remediation of Hydrocarbon-Contaminated Soil by Washing with Novel Chemically Modified Humic Substances.

In this work, humic (HA) and fulvic acid (FA) were chemically modified by esterification and etherification with alkanes under microwave (MW) irradiation to improve their surfactant properties for the remediation of total petroleum hydrocarbons (TPHs)-contaminated soil. Humic acid and FA were evaluated as surfactant for the remediation of soil by means of washing an aged highly TPH-contaminated soil (50,000 mg TPH kg) sampled from a Mexican petrochemical area. The efficiency of chemical modification of HA and FA was increased and accelerated under MW irradiation with respect to that of conventional heating. Results showed that modified HA and FA were able to considerably reduce the contamination of TPH-polluted soils. The best results were obtained with HA modified by esterification with -dodecanol and FA modified with -decanol, which increased the hydrocarbon removal by 24 and 18%, respectively, with respect to amounts removed by the unmodified derivatives.