Essays in optimal resource allocation under uncertainty with capacity constraints

This thesis brings together four papers on optimal resource allocation under uncertainty with capacity constraints. The first is an extension of the Arrow-Debreu contingent claim model to a good subject to supply uncertainty for which delivery capacity has to be chosen before the uncertainty is resolved. The second compares an ex-ante contingent claims market to a dynamic market in which capacity is chosen ex-ante and output and consumption decisions are made ex-post. The third extends the analysis to a storable good subject to random supply. Finally, the fourth examines optimal allocation of water under an appropriative rights system.

The heat capacity of superfluid ^4He in the presence of a constant heat flux near Tλ

We present the first experimental evidence that the heat capacity of superfluid ⁴He, at temperatures very close to the lambda transition temperature, T_λ,is enhanced by a constant heat flux, Q. The heat capacity at constant Q, C_Q,is predicted to diverge at a temperature T_c(Q) < T_λ at which superflow becomes unstable. In agreement with previous measurements, we find that dissipation enters our cell at a temperature, T_DAS(Q),below the theoretical value, T_c(Q). Our measurements of C_Q were taken using the discrete pulse method at fourteen different heat flux values in the range 1µW/cm² ≤ Q≤ 4µW /cm². The excess heat capacity ∆C_Q we measure has the predicted scaling behavior as a function of T and Q:∆C_Q • t^α ∝ (Q/Q_c)², where Q_cT) ~ t^2ν is the critical heat current that results from the inversion of the equation for T_c(Q). We find that if the theoretical value of T_c( Q) is correct, then ∆C_Q is considerably larger than anticipated. On the other hand,if T_c(Q)≈ T_DAS(Q),then ∆C_Q is the same magnitude as the theoretically predicted enhancement.

Information-theoretic Studies and Capacity Bounds: Group Network Codes and Energy Harvesting Communication Systems

Network information theory and channels with memory are two important but difficult frontiers of information theory. In this two-parted dissertation, we study these two areas, each comprising one part. For the first area we study the so-called entropy vectors via finite group theory, and the network codes constructed from finite groups. In particular, we identify the smallest finite group that violates the Ingleton inequality, an inequality respected by all linear network codes, but not satisfied by all entropy vectors. Based on the analysis of this group we generalize it to several families of Ingleton-violating groups, which may be used to design good network codes. Regarding that aspect, we study the network codes constructed with finite groups, and especially show that linear network codes are embedded in the group network codes constructed with these Ingleton-violating families. Furthermore, such codes are strictly more powerful than linear network codes, as they are able to violate the Ingleton inequality while linear network codes cannot. For the second area, we study the impact of memory to the channel capacity through a novel communication system: the energy harvesting channel. Different from traditional communication systems, the transmitter of an energy harvesting channel is powered by an exogenous energy harvesting device and a finite-sized battery. As a consequence, each time the system can only transmit a symbol whose energy consumption is no more than the energy currently available. This new type of power supply introduces an unprecedented input constraint for the channel, which is random, instantaneous, and has memory. Furthermore, naturally, the energy harvesting process is observed causally at the transmitter, but no such information is provided to the receiver. Both of these features pose great challenges for the analysis of the channel capacity. In this work we use techniques from channels with side information, and finite state channels, to obtain lower and upper bounds of the energy harvesting channel. In particular, we study the stationarity and ergodicity conditions of a surrogate channel to compute and optimize the achievable rates for the original channel. In addition, for practical code design of the system we study the pairwise error probabilities of the input sequences.