Eight chapters examine cognitive processes underlying three moral judgments: how much harm is acceptable for greater good, how much to help others, and how responsible to hold them for their successes and failures. Chapters 1-3 examine how judgments of actions that cause harm to achieve a greater good are sensitive to expected value (the ratio of good done to harm done), outcome likelihoods, and where shifts in outcome likelihoods occur. Findings contradict dominant dual-process theories of moral cognition, which posit that people either react to the harm caused by the action or to the net benefit resulting from it, irrespective of the specific ratio of harm done to good done. We demonstrate that moral judgments are remarkably sensitive to this ratio, in ways partially consistent with Prospect Theory. Chapter 4 provides further evidence for the interaction of affective and deliberative processes by demonstrating how incidental affect can shift moral risk preferences.

Chapter 5 explores the mental representation of good deeds. The proposed Moral Accounting Model illustrates how moral credit from prior beneficence excuses further beneficence. Effort, effect, domain generalizability, temporal generalizability, and temporal diffusion are identified as features of moral credit. Chapter 6 identifies the extent to which people care about the effectiveness of their beneficence: Though donors prefer to give to more efficient charities of the options they are presented with, whether the options explicitly fail to meet or exceed efficiency standards does not affect donor behavior.

Chapter 7 examines responsibility attribution, challenging a prevalent view in lay theory research that thinking of people as changeable is universally adaptive. It provides a theoretical argument for how viewing people as changeable may result in holding others increasingly personally responsible for their circumstances. Chapter 8 provides empirical evidence for this process: the same mindset inductions used to demonstrate the benefits of changeability are shown to increase blame of others for continual failures.

Implications for real-world decision-making, from how to program autonomous vehicles to avoid collisions, to how to encourage donation to charity, to how to address structural barriers to achievement are discussed.