arXiv subjects

Stephan Leitner

Publications and source records attributed to Stephan Leitner.

At least 19 recordsLinked to original sources

arXiv2025

Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation

Personalized news recommendations have become a standard feature of large news aggregation services, optimizing user engagement through automated content selection. In contrast, legacy news media often approach personalization cautiously, striving to balance technological innovation with core editorial values. As a result, online platforms of traditional news outlets typically combine editorially curated content with algorithmically selected articles - a strategy we term controlled personalization. In this industry article, we evaluate the effectiveness of controlled personalization through an A/B test conducted on the website of a major Norwegian legacy news organization. Our findings indicate that even a modest level of personalization yields substantial benefits. Specifically, we observe that users exposed to personalized content demonstrate higher click-through-rates and reduced navigation effort, suggesting improved discovery of relevant content. Moreover, our analysis reveals that controlled personalization contributes to greater content diversity and catalog coverage and in addition reduces popularity bias. Overall, our results suggest that controlled personalization can successfully align user needs with editorial goals, offering a viable path for legacy media to adopt personalization technologies while upholding journalistic values.

cs.IR↗

arXiv2025

Conformity: Resolving the Trade-Off Between Performance and Synchrony in Multi-Unit Organizations

Multi-unit organizations are a form of organizations where the geographically dispersed units provide similar products or services in different markets. Deciding on an appropriate level of centralization in such organizations presents a unique challenge. One the one hand the organizations want to maintain a consistent brand identity in all units through centralized control, but on the other hand, they want to provide the units with sufficient autonomy to respond to the challenges they face locally. Traditionally, this challenge was perceived to require a trade-off between performance and organizational synchrony, with performance demanding more decentralization and synchrony requiring more centralized control. However, our research explores how organizations can potentially resolve this trade-off by promoting norms for knowledge-sharing and setting up the right communication channels, relying on the unit managers' intrinsic tendency to conform to the behavior of their peers. We build an agent-based model of an organization with multiple interdependent units facing highly similar task environments to investigate how unit managers' ability to communicate, share knowledge, and conform to peer practices might influence organizational dynamics. We find that, under specific communication network structures, increased decentralization can enhance both performance and organizational synchrony without sacrificing one or the other. Furthermore, we discover that centralization might still be preferable for synchrony if the units are interdependent.

econ.GN↗

arXiv2024

Interactions between dynamic team composition and coordination: An agent-based modeling approach

This paper examines the interactions between selected coordination modes and dynamic team composition, and their joint effects on task performance under different task complexity and individual learning conditions. Prior research often treats dynamic team composition as a consequence of suboptimal organizational design choices. The emergence of new organizational forms that consciously employ teams that change their composition periodically challenges this perspective. In this paper, we follow the contingency theory and characterize dynamic team composition as a design choice that interacts with other choices such as the coordination mode, and with additional contextual factors such as individual learning and task complexity. We employ an agent-based modeling approach based on the NK framework, which includes a reinforcement learning mechanism, a recurring team formation mechanism based on signaling, and three different coordination modes. Our results suggest that by implementing lateral communication or sequential decision-making, teams may exploit the benefits of dynamic composition more than if decision-making is fully autonomous. The choice of a proper coordination mode, however, is partly moderated by the task complexity and individual learning. Additionally, we show that only a coordination mode based on lateral communication may prevent the negative effects of individual learning.

econ.GN↗

arXiv2023

Building resilient organizations: The roles of top-down vs. bottom-up organizing

Organizations face numerous challenges posed by unexpected events such as energy price hikes, pandemic disruptions, terrorist attacks, and natural disasters, and the factors that contribute to organizational success in dealing with such disruptions often remain unclear. This paper analyzes the roles of top-down and bottom-up organizational structures in promoting organizational resilience. To do so, an agent-based model of stylized organizations is introduced that features learning, adaptation, different modes of organizing, and environmental disruptions. The results indicate that bottom-up designed organizations tend to have a higher ability to absorb the effects of environmental disruptions, and situations are identified in which either top-down or bottom-up designed organizations have an advantage in recovering from shocks.

econ.GN↗

arXiv2023

Designing organizations for bottom-up task allocation: The role of incentives

In recent years, various decentralized organizational forms have emerged, posing a challenge for organizational design. Some design elements, such as task allocation, become emergent properties that cannot be fully controlled from the top down. The central question that arises in this context is: How can bottom-up task allocation be guided towards an effective organizational structure? To address this question, this paper presents a novel agent-based model of an organization that features bottom-up task allocation that can be motivated by either long-term or short-term orientation on the agents' side. The model also includes an incentive mechanism to guide the bottom-up task allocation process and create incentives that range from altruistic to individualistic. Our analysis shows that when bottom-up task allocation is driven by short-term orientation and aligned with the incentive mechanisms, it leads to improved organizational performance that surpasses that of traditionally designed organizations. Additionally, we find that the presence of altruistic incentive mechanisms within the organization reduces the importance of mirroring in task allocation.

econ.GN↗

arXiv2022

The benefits of coordination in (over)adaptive virtual teams

The emergence of new organizational forms--such as virtual teams--has brought forward some challenges for teams. One of the most relevant challenges is coordinating the decisions of team members who work from different time zones. Intuition suggests that task performance should improve if the team members' decisions are coordinated. However, previous research suggests that the effect of coordination on task performance is ambiguous. Specifically, the effect of coordination on task performance depends on aspects such as the team members' learning and the changes in team composition over time. This paper aims to understand how individual learning and team composition moderate the relationship between coordination and task performance. We implement an agent-based modeling approach based on the NK-framework to fulfill our research objective. Our results suggest that both factors have moderating effects. Specifically, we find that excessively increasing individual learning is harmful for the task performance of fully autonomous teams, but less detrimental for teams that coordinate their decisions. In addition, we find that teams that coordinate their decisions benefit from changing their composition in the short-term, but fully autonomous teams do not. In conclusion, teams that coordinate their decisions benefit more from individual learning and dynamic composition than teams that do not coordinate. Nevertheless, we should note that the existence of moderating effects does not imply that coordination improves task performance. Whether coordination improves task performance depends on the interdependencies between the team members' decisions.

econ.GN↗

arXiv2022

Controlling replication via the belief system in multi-unit organizations

Multi-unit organizations such as retail chains are interested in the diffusion of best practices throughout all divisions. However, the strict guidelines or incentive schemes may not always be effective in promoting the replication of a practice. In this paper we analyze how the individual belief systems, namely the desire of individuals to conform, may be used to spread knowledge between departments. We develop an agent-based simulation of an organization with different network structures between divisions through which the knowledge is shared, and observe the resulting synchrony. We find that the effect of network structures on the diffusion of knowledge depends on the interdependencies between divisions, and that peer-to-peer exchange of information is more effective in reaching synchrony than unilateral sharing of knowledge from one division. Moreover, we find that centralized network structures lead to lower performance in organizations.

econ.GN↗

arXiv2022

Collaborative search and autonomous task allocation in organizations of learning agents

This paper introduces a model of multi-unit organizations with either static structures, i.e., they are designed top-down following classical approaches to organizational design, or dynamic structures, i.e., the structures emerge over time from micro-level decisions. In the latter case, the units are capable of learning about the technical interdependencies of the task they face, and they use their knowledge by adapting the task allocation from time to time. In both static and dynamic organizations, searching for actions to increase the performance can either be carried out individually or collaboratively. The results indicate that (i) collaborative search processes can help overcome the adverse effects of inefficient task allocations as long as there is an internal fit with other organizational design elements, and (ii) for dynamic organizations, the emergent task allocation does not necessarily mirror the technical interdependencies of the task the organizations face, even though the same (or even higher) performances are achieved.

econ.GN↗

arXiv2022

Dynamic groups in complex task environments: To change or not to change a winning team?

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 comes to ambiguous results regarding the effects of group adaptation on task performance. This paper aims to understand the impact of group adaptation, defined as a process of periodically changing a group's composition, on complex task performance and considers the moderating role of individual learning and task complexity in this relationship. We base our analyses on an agent-based model of adaptive groups in a complex task environment based on the NK-framework. The 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, thereby, help explain the ambiguous results of previous research.

econ.GN↗

arXiv2022

Interactions between the individual and the group level in organizations: The case of learning and autonomous group adaptation

Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation and adaptation. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group that can be adapted from time to time. We explore organizations that promote learning and group adaptation either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group adaptation too far might backfire and decrease performance significantly.

econ.GN↗

arXiv2022

Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis

Automated recommendations can nowadays be found on many e-commerce platforms, and such recommendations can create substantial value for consumers and providers. Often, however, not all recommendable items have the same profit margin, and providers might thus be tempted to promote items that maximize their profit. In the short run, consumers might accept non-optimal recommendations, but they may lose their trust in the long run. Ultimately, this leads to the problem of designing balanced recommendation strategies, which consider both consumer and provider value and lead to sustained business success. This work proposes a simulation framework based on agent-based modeling designed to help providers explore longitudinal dynamics of different recommendation strategies. In our model, consumer agents receive recommendations from providers, and the perceived quality of the recommendations influences the consumers' trust over time. We design several recommendation strategies which either give more weight on provider profit or on consumer utility. Our simulations show that a hybrid strategy that puts more weight on consumer utility but without ignoring profitability considerations leads to the highest cumulative profit in the long run. This hybrid strategy results in a profit increase of about 20 % compared to pure consumer or profit oriented strategies. We also find that social media can reinforce the observed phenomena. In case when consumers heavily rely on social media, the cumulative profit of the best strategy further increases. To ensure reproducibility and foster future research, we publicly share our flexible simulation framework.

cs.SI↗

arXiv2021

Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation

Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users. In reality, however, various important and interesting phenomena only emerge or become visible over time, e.g., when a recommender system continuously reinforces the popularity of already successful artists on a music streaming site or when recommendations that aim at profit maximization lead to a loss of consumer trust in the long run. In this paper, we discuss how Agent-Based Modeling and Simulation (ABM) techniques can be used to study such important longitudinal dynamics of recommender systems. To that purpose, we provide an overview of the ABM principles, outline a simulation framework for recommender systems based on the literature, and discuss various practical research questions that can be addressed with such an ABM-based simulation framework.

cs.IR↗

arXiv2021

Micro-level dynamics in hidden action situations with limited information

The hidden-action model provides an optimal sharing rule for situations in which a principal assigns a task to an agent who makes an effort to carry out the task assigned to him. However, the principal can only observe the task outcome but not the agent's actual action, which is why the sharing rule can only be based on the outcome. The hidden-action model builds on somewhat idealized assumptions about the principal's and the agent's capabilities related to information access. We propose an agent-based model that relaxes some of these assumptions. Our analysis lays particular focus on the micro-level dynamics triggered by limited access to information. For the principal's sphere, we identify the so-called Sisyphus effect that explains why the sharing rule that provides the agent with incentives to take optimal action is difficult to achieve if the information is limited, and we identify factors that moderate this effect. In addition, we analyze the behavioral dynamics in the agent's sphere. We show that the agent might make even more of an effort than optimal under unlimited access to information, which we refer to as excess effort. Interestingly, the principal can control the probability of making an excess effort via the incentive mechanism. However, how much excess effort the agent finally makes is out of the principal's direct control.

econ.GN↗

arXiv2021

Limited intelligence and performance-based compensation: An agent-based model of the hidden action problem

Models of economic decision makers often include idealized assumptions, such as rationality, perfect foresight, and access to all relevant pieces of information. These assumptions often assure the models' internal validity, but, at the same time, might limit the models' power to explain empirical phenomena. This paper is particularly concerned with the model of the hidden action problem, which proposes an optimal performance-based sharing rule for situations in which a principal assigns a task to an agent, and the action taken to carry out this task is not observable by the principal. We follow the agentization approach and introduce an agent-based version of the hidden action problem, in which some of the idealized assumptions about the principal and the agent are relaxed so that they only have limited information access, are endowed with the ability to gain information, and store it in and retrieve it from their (limited) memory. We follow an evolutionary approach and analyze how the principal's and the agent's decisions affect the sharing rule, task performance, and their utility over time. The results indicate that the optimal sharing rule does not emerge. The principal's utility is relatively robust to variations in intelligence, while the agent's utility is highly sensitive to limitations in intelligence. The principal's behavior appears to be driven by opportunism, as she withholds a premium from the agent to assure the optimal utility for herself.

econ.GN↗

arXiv2021

Effects of limited and heterogeneous memory in hidden-action situations

Limited memory of decision-makers is often neglected in economic models, although it is reasonable to assume that it significantly influences the models' outcomes. The hidden-action model introduced by Holmstr\"om also includes this assumption. In delegation relationships between a principal and an agent, this model provides the optimal sharing rule for the outcome that optimizes both parties' utilities. This paper introduces an agent-based model of the hidden-action problem that includes limitations in the cognitive capacity of contracting parties. Our analysis mainly focuses on the sensitivity of the principal's and the agent's utilities to the relaxed assumptions. The results indicate that the agent's utility drops with limitations in the principal's cognitive capacity. Also, we find that the agent's cognitive capacity limitations affect neither his nor the principal's utility. Thus, the agent bears all adverse effects resulting from limitations in cognitive capacity.

econ.GN↗

arXiv2021

On the Role of Incentives in Evolutionary Approaches to Organizational Design

This paper introduces a model of a stylized organization that is comprised of several departments that autonomously allocate tasks. To do so, the departments either take short-sighted decisions that immediately maximize their utility or take long-sighted decisions that aim at minimizing the interdependencies between tasks. The organization guides the departments' behavior by either an individualistic, a balanced, or an altruistic linear incentive scheme. Even if tasks are perfectly decomposable, altruistic incentive schemes are preferred over individualistic incentive schemes since they substantially increase the organization's performance. Interestingly, if altruistic incentive schemes are effective, short-sighted decisions appear favorable since they do not only increase performance in the short run but also result in significantly higher performances in the long run.

econ.GN↗

arXiv2021

On the effect of social norms on performance in teams with distributed decision makers

Social norms are rules and standards of expected behavior that emerge in societies as a result of information exchange between agents. This paper studies the effects of emergent social norms on the performance of teams. We use the NK-framework to build an agent-based model, in which agents work on a set of interdependent tasks and exchange information regarding their past behavior with their peers. Social norms emerge from these interactions. We find that social norms come at a cost for the overall performance, unless tasks assigned to the team members are highly correlated, and the effect is stronger when agents share information regarding more tasks, but is unchanged when agents communicate with more peers. Finally, we find that the established finding that the team-based incentive schemes improve performance for highly complex tasks still holds in presence of social norms.

econ.GN↗

arXiv2021

Multi-level Adaptation of Distributed Decision-Making Agents in Complex Task Environments

To solve complex tasks, individuals often autonomously organize in teams. Examples of complex tasks include disaster relief rescue operations or project development in consulting. The teams that work on such tasks are adaptive at multiple levels: First, by autonomously choosing the individuals that jointly perform a specific task, the team itself adapts to the complex task at hand, whereby the composition of teams might change over time. We refer to this process as self-organization. Second, the members of a team adapt to the complex task environment by learning. There is, however, a lack of extensive research on multi-level adaptation processes that consider self-organization and individual learning as simultaneous processes in the field of management science. We introduce an agent-based model based on the NK-framework to study the effects of simultaneous multi-level adaptation on a team's performance. We implement the multi-level adaptation process by a second-price auction mechanism for self-organization at the team level. Adaptation at the individual level follows an autonomous learning mechanism. Our preliminary results suggest that, depending on the task's complexity, different configurations of individual and collective adaptation can be associated with higher overall task performance. Low complex tasks favour high individual and collective adaptation, while moderate individual and collective adaptation is associated with better performance in case of moderately complex tasks. For highly complex tasks, the results suggest that collective adaptation is harmful to performance.

econ.GN↗