Collaboration enables multiple actors with different objectives to work together and achieve a goal beyond individual capabilities. However, strategic uncertainty from partners’ actions introduces a potential for losses under failed collaboration relative to pursuing an independent system. The fundamental tradeoff between high-value but uncertain outcomes from collaborative systems and lower-value but more certain outcomes for independent systems induces a bistability strategic dynamic. Actors exhibit different risk attitudes that impact decisions under uncertainty which complicate shared understanding of collaborative dynamics. This paper investigates how risk attitudes affect design and strategy decisions in collaborative systems through the lens of game theory. First, an analytical model studies the effect of differential risk attitudes in a two-actor problem with stag-hunting strategic dynamics formulated as single- and bi-level games. Next, a simulation model pairs actors with different risk attitudes in a 29-game tournament based on a prior behavioral experiment. Results show that outcomes collaborative design problems change based on the risk attitudes of both actors. Results also emphasize that considering conservative lower-level design options facilitates collaboration by providing risk-averse actors with a safer solution. By accepting that decision-making actors are not all risk-neutral, future work seeks to develop new design methods to strengthen the adoption of efficient collaborative solutions.
Data-driven operations management methods can transform company operations, respond rapidly to customer demands, and enable new business models. However, companies face the challenge of measuring and evaluating how new technology will impact operational processes. This paper takes a systems engineering approach to assess the tradeoffs of adopting data-driven mechanisms to improve operational processes in a multichannel customer support system. We investigate potential cost savings from two technology applications: classification methods to direct customers to efficient self-service communication channels and routing methods to match customers with agents based on the query type and available skill set. Discrete event simulation evaluates how new technology adoption affects system-level performance. What-if scenarios combine distinct configurations of customer classification mechanisms and available communication channels to evaluate the reduction in the total number of agents required to meet a target service quality level. Discussion includes practical examples of how operational managers could use experimental information to make strategic operational decisions when adopting data-driven technologies.
The New Observing Strategies Testbed (NOS-T) is a digital engineering environment for enabling distributed space mission (DSM) technology demonstrations. Its event-driven architecture enables users to orchestrate DSM test campaigns by developing applications that communicate state changes via messages. NOS-T is motivated by requirements such as geographical distribution, cross-boundary participation, wide applicability, and usability that make it unique in this field. This article introduces NOS-T and describes its architecture in the context of an example DSM test suite, FireSat+. The scalability of NOS-T is demonstrated with a performance assessment of its capabilities under a stress test of high message frequency and payload size, which are both related to the complexity of potential user-generated test cases. Results show that message periodicity has no significant effect on median delay time over the ranges sampled; however, the message payload size induces linear growth in median delay time of approximately 1.5 ms per kB. Future NOS-T applications can adjust the execution time scaling factor and message payload size to match operational constraints on allowable delay.
Internet satellite constellations are expected to play an important role in accommodating the rising global demand for internet access. Such rise in demand, however, is highly uncertain. Staged deployment is an approach that provides flexibility to tackle demand uncertainty by enabling the real option to reconfigure a constellation if demand changes. Advancements in satellite technology have led to the emergence of multi-layered constellations. This opens the opportunity to enhance staged deployment by enabling an additional real option: adding a new layer to a constellation. This real option has no associated reconfiguration costs, and therefore has the potential to reduce the cost of staged systems deployment. This paper proposes a framework to design multi-layer staged deployment systems and analyse their effectiveness in modern mega-constellations under global demand uncertainty. The framework is applied to four case studies based on market projections. Results show that multi-layer staged deployment decreases the expected life-cycle cost (ELCC) by 42.8% compared to optimal traditional single-layer deployment. Multi-layer staged deployment is more cost effective than single-layer staged deployment in all practical cases, which decreases ELCC by 22.9% compared to traditional deployment. Several cost altering mechanisms in staged deployment are identified. The results and analysis provide improved economic performance and better resource utilization, thus contributing in the long term to improved sustainability and market resilience. An accompanying decision support system provides system engineers with valuable insights on how to reduce deployment costs using the proposed multi-layered staged strategy.
Motivated by the growth of the commercial space economy and renewed focus on the disaggregation of military space systems, this work develops a method for conceptual design of federated satellite systems as a collaborative system of systems (SOS). Objectives seek to improve the likelihood of successful SOS formation and pursue constituent system utility robustness. The proposed metaheuristic optimization trade-space exploration method accounts for technical and economic design variables and multi-decision-maker strategy dynamics. Constituent system designs are ranked on their simulated net present value. A game-theoretic measure of risk dominance is used in concert with the net present value to assess the robustness and utility of candidate SOS designs. The method is validated with a notional application case that assesses potential collaboration between Earth-observing and telecommunications systems. The proposed methodology reduces the threshold probability of partner collaboration for which SOS participation is economically rational by up to 18% for the most efficient designs as compared to a typical conceptual design method, thereby increasing the likelihood of successful SOS formation. The results highlight the importance of accounting for strategy dynamics when designing systems for collaboration.
Robust designs protect system utility in the presence of uncertainty in technical and operational outcomes. Systems-of-systems, which lack centralized managerial control, are vulnerable to strategic uncertainty from coordination failures between partially or completely independent system actors. This work assesses the suitability of a game-theoretic equilibrium selection criterion to measure system robustness to strategic uncertainty and investigates the effect of strategically robust designs on collaborative behavior. The work models interactions between agents in a thematic representation of a mobile computing technology transition using an evolutionary game theory framework. Strategic robustness and collaborative solutions are assessed over a range of conditions by varying agent payoffs. Models are constructed on small world, preferential attachment and random graph topologies and executed in batch simulations. Results demonstrate that systems designed to reduce the impacts of coordination failure stemming from strategic uncertainty also increase the stability of the collaborative strategy by increasing the probability of collaboration by partners; a form of robustness by environment shaping that has not been previously investigated in design literature. The work also demonstrates that strategy selection follows the risk dominance equilibrium selection criterion and that changes in robustness to coordination failure can be measured with this criterion.
A new research paper co-authored by Leigha Capra, Jay Hilton, Sarah Bentley, Theodore Sherman, Aaron Alfaro, Ryan Savin, Olivier de Weck and Paul Grogan appears in the AIAA ASCEND 2021 conference proceedings. The paper will be presented on November 10, 2021.
The advancing digital engineering landscape generates a need for modern human space exploration logistics planning tools. The goal of the SpaceNet Cloud project is to build a tool to satisfy this need through a dynamic web-based application based on the existing SpaceNet space logistics tool. SpaceNet Cloud condenses the process of organizing, constructing, and analyzing a mission scenario into a user-friendly web-based application. A simplistic interface, coupled with powerful backend capabilities allows SpaceNet Cloud to harness the accessibility of cloud-based computing, creating a modern take on mission logistics. The effectiveness of a user’s mission is clearly defined using an incremental mission outline process, and a clear visualization of demand analysis upon completion. The dynamic nature of the application also allows for rapid prototyping of missions based on final analysis results, and the potential for collaborative design opens opportunities for public and private sectors alike.
This chapter focuses on strategies for technical design of engineering systems. The strategies allow designers to manage the complexity arising from the interconnected nature of engineering systems, while achieving both technical and business objectives. The design strategies discussed in the chapter include hierarchical decomposition, modularity, design for emergent behaviors, modeling and simulation, and optimization-based strategies. Hierarchical decomposition forms the basis for traditional top-down systems engineering processes where the overall system is decomposed into quasi-independent modules which can be developed concurrently and integrated into the overall system. While decomposition-based approaches are ideally suited for achieving functional properties of the system, they do not provide guidance for achieving emergent properties. The strategies for design of emergent properties include design for quality, design for changeability, and, more generally, design for X. To support both top-down functional design and design for emergent properties, commonly used modeling and simulation approaches, and optimization-based approaches are discussed. The chapter discusses challenges and trade-offs in designing complex engineering systems for technical behavior, such as complexity vs. robustness, requirements vs. value, modularity vs. performance, and the interactions between social and technical aspects.
Human exploration logistics rely on a launch vehicle to place supplies in orbit. Estimating launch vehicle delay helps mission planning ensure adequate supplies under uncertainty in replenishment schedule. This paper mines launch delay data for human exploration missions from the International Space Station (ISS) US operating segment (USOS) including NASA commercial cargo (Northrop Grumman and SpaceX), ESA and JAXA missions from March 2013 to February 2017 as a mix of established mission providers (ESA and JAXA) and commercial companies spanning launch vehicle system development and recurring cargo delivery missions. Continuous probability distributions are developed using maximum likelihood estimates for launch delays associated with near-term, intermediate and long-term mission planning dates. Additionally, an approach adapted from the signal processing domain to convert the continuous distribution into a discrete probability mass function is outlined for scenario tree analysis.
This paper draws on perspectives from co-design as an integrative and collaborative design activity and co-simulation as a supporting information system to advance engineering design methods for problems of societal significance. Design and implementation of the Sustainable Infrastructure Planning Game provides a prototypical co-design artifact that leverages the High Level Architecture co-simulation standard. Three role players create a strategic infrastructure plan for agricultural, water and energy sectors to meet sustainability objectives for a growing and urbaninzing population in a fictional desert nation. An observational study conducts 15 co-design sessions to understand underlying dynamics between actors and how co-simulation capabilities influence design outcomes. Results characterize the dependencies and conflicts between player roles based on technical exchange of resource flows, identifying tension between agriculture and water roles based on water demands for irrigation. Analysis shows a correlation between data exchange, facilitated by synchronous co-simulation, and highly ranked achievement of joint sustainability outcomes. Conclusions reflect on the opportunities and challenges presented by co-simulation in co-design settings to address engineering systems problems.