Is altruism evolutionarily stable ?

We develop an evolutionary approach to explain altruistic preferences. Given their preferences, individuals interact rationally with each other. By comparing the success of players with different preferences, we investigate whether evolution favors altruistic or selfish attitudes. The outcome depends on whether the individuals' interactions are strategic complements or substitutes. Altruism and self-interest are context dependent.

Evolution & voting : how nature makes us public spirited

We reconsider the classic puzzle of why election turnouts are persistently so high even though formal analysis strongly suggests that rational agents should not vote. If we assume that voters are not making systematic mistakes, the most plausible explanation seems to be that agents receive benefits from the act of voting itself. This is very close to assuming the answer, however, and immediately begs the question of why agents feel a warm glow from participating in the electoral process. In this paper, we approach the question from an evolutionary standpoint. We show for a range of situations, that public-spirited agents have an evolutionary advantage over those who are not as public-spirited. We also explore conditions under which this kind of altruistic behavior is disadvantageous to agents. The details depend on the costs of voting, the degree to which different types of agents have different preferences over public policies and the relative proportions of various preference types in the population, but we conclude that evolution may often be a force that causes agents to internalize the benefits their actions

Ethical theory and stakeholder-related decisions: The role of stakeholder culture

We use convergent elements of major ethical theories to create a typology of corporate stakeholder cultures—the aspects of organizational culture consisting of the beliefs, values, and practices that have evolved for solving problems and otherwise managing stakeholder relationships. We describe five stakeholder cultures—agency, corporate egoist, instrumentalist, moralist, and altruist—and explain how these cultures lie on a continuum, ranging from individually self-interested (agency culture) to fully other-regarding (altruist culture). We demonstrate the utility of our framework by showing how it can refine stakeholder salience theory

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications