<div>Space mission-related projects are demanding and risky undertakings because of their complexity and cost. Many missions have failed over the years due to anomalies in either the launch vehicle or the spacecraft. Projects of such magnitude with undetected flaws due to ineffective process controls run into unwarranted cost, schedule overruns and account for huge losses. Such failures continue to occur despite the studies on systems engineering process deficiencies and the best systems engineering practices in place. To understand the reasons behind such failures, this work analyses some of the major contributing factors behind majority of space mission technical failures. To achieve this objective, we analyzed the failure data of space missions that happened over the last decade. Based on that information, we analyzed the launch-related failure events from a design decision-making perspective by employing failure event chain-based framework. By analyzing the failure events with this framework, we identify some dominant cognitive biases that might have impacted the overall system performance leading to unintended catastrophes. </div><div><br></div><div>The ability of any design team to achieve optimal performance is limited by communication and knowledge deficiencies between highly dissimilar subsystems. These inefficiencies work to bias each subsystem engineer to prioritize the utility provided by the subsystem they are responsible for. In order to understand how engineering design decisions are influenced by the presence of cognitive biases, the second part of this study establishes a mathematical framework for utility-based selection based on Cumulative Prospect Theory. This framework captures the effect of cognitive biases on selection of alternatives by a rational decision-maker.</div><div><br></div><div>From the first study, overconfidence and anchoring biases are identified as the two dominant contributing factors that influenced the decisions behind majority of the failures. The theoretical models developed in the second study are employed to depict the influence of biased decision-making on utility-based selection of alternatives for an earth-orbiting satellite's power subsystem. Predictions from these models show a direct correlation between the decision-maker's biased preference structure and local change in utility curve depicting the (negative) influence of cognitive biases on decision-maker's choice(s).</div>