Ellsberg Paradox and Second-order Preference Theories on Ambiguity: Some New Experimental Evidence

We study the two-color problem by Ellsberg (1961) with the modification that the decision maker draws twice with replacement and a different color wins in each draw. The 50-50 risky urn turns out to have the highest risk conceivable among all prospects including the ambiguous one, while all feasible color distributions are mean-preserving spreads to one another. We show that the well-known second-order sophisticated theories like MEU, CEU, and REU as well as Savage’s first-order theory of SEU share the same predictions in our design, for any first-order risk attitude. Yet, we observe that substantial numbers of subjects violate the theory predictions even in this simple design

Ellsberg Paradox and Second-order Preference Theories on Ambiguity: Some New Experimental Evidence

We study the two-color problem by Ellsberg (1961) with the modification that the decision maker draws twice with replacement and a different color wins in each draw. The 50-50 risky urn turns out to have the highest risk conceivable among all prospects including the ambiguous one, while all feasible color distributions are mean-preserving spreads to one another. We show that the well-known second-order sophisticated theories like MEU, CEU, and REU as well as Savage’s first-order theory of SEU share the same predictions in our design, for any first-order risk attitude. Yet, we observe that substantial numbers of subjects violate the theory predictions even in this simple design.Ellsberg paradox, Ambiguity, Second-order risk, Second-order preference theory, Experiment

Mid-infrared emitter technology.

As part of an effort to improve heat dissipation from mid-infrared laser materials during operation, work was performed on mounting laser bars in an epi-side-down configuration. Results obtained from packaging interband cascade laser (ICL) bars provided by the Jet Propulsion Laboratory (JPL) were partially successful. Epi-side down packaged devices exhibited current-versus-voltage characteristics similar to epi-side up packaged devices, but mid-infrared emission was not observed when mounted devices were tested using a Fourier transform infrared (FTIR) spectrometer.To characterize the optical emission of the mid-infrared emitters, a filter wheel based mid-infrared spectrometer was developed to detect mid-infrared radiation between 1875 cm-1 and 3627 cm-1. Photoluminescence (PL) tests of a molecular beam epitaxy (MBE) grown IV-VI quantum well sample (MBE# W336) were performed using the filter wheel based mid-infrared spectrometer. By comparing the PL emission intensity to blackbody emission spectra, the cw PL emission power from the IV-VI quantum well sample at room temperature was estimated to be 0.183 mW when illuminated with a near-IR (911 nm) pump laser having a power of 970 mW.Mid-infrared emitters (3--30 microm spectral region) have numerous applications in spectroscopy and communications. Two major types of semiconductor material systems offer great potential for the fabrication of mid-infrared emitters with continuous wave (cw) operation at room temperature: IV-VI materials (lead salts) and III-V quantum cascade laser materials. Compared with quantum cascade lasers, IV-VI double heterostructure lasers are easier to fabricate, are widely tunable, and operate at lower voltages.Mid-infrared emission spectra for epi-side up packaged JPL ICLs measured using FTIR spectroscopy are presented and analyzed. Injection current tuning for ICL #J653 at 86 K was observed to be 0.10 cm-1/mA, while the tuning rate for ICL #J435 at 80 K was 1.2 cm-1/mA. This difference may be caused by different sized mesa stripes. ICL #J653 has a mesa width of 15 microm and a cavity length of 1.5 mm, while ICL #J435 has a mesa width of 150 microm and a cavity length of 1 mm. A smaller current tuning rate is consistent with less active region heating, and ICL #J653 with its thinner mesa width will have more effective active region heat dissipation due to better lateral heat flow normal to the laser cavity. (Abstract shortened by UMI.

Sediment yield of South Korean rivers, The

2019 Spring.Includes bibliographical references.South Korea is experiencing increasing river sedimentation problems, which requires a reliable method to predict the sediment yield. With the recent field measurements at 35 gaging stations in South Korea provided by K-water, we quantified the sediment yield by using the flow duration curve and sediment rating curve. The current sediment yield models have large discrepancies between the predictions and measurements. The goal of this dissertation is to provide better understanding to the following questions: (1) How much of the total sediment load can be measured by the depth-integrated samplers? (2) Can we predict the sediment yield based only on watershed area? (3) Is there a parametric approach to estimate the mean annual sediment yield based on the flow duration curve and sediment rating curve? With 1,962 sediment discharge measurements from the US D-74 sampler, the total sediment discharge is calculated by both the Modified Einstein Procedure (MEP) and the Series Expansion of the Modified Einstein Procedure (SEMEP). It is concluded that the SEMEP is more accurate because MEP occasionally computes suspended loads larger than total loads. In addition, SEMEP was able to calculate all samples while MEP could only compute 1,808 samples. According to SEMEP, the ratio Qm/Qt of measured sediment discharge Qm to total sediment discharge Qt is a function of the Rouse number Ro, flow depth h, and the median grain size of the bed material d50. In Korean sand and gravel bed rivers, the materials in suspension are fine (silt or clay) and Ro ≈ 0. The ratio Qm/Qt reduces to a function of flow depth h, and at least 90% of the total sediment load is measured when h > 1 m. More than 80% of the sediment load is measured when the discharge Q is larger than four times mean annual discharge ¯Q(Q/¯Q > 4). The ratio Qs/Qt of suspended sediment discharge Qs to total sediment discharge can be also analyzed with SEMEP and the result shows that Qs/Qt is a function of h/d50 and Ro. When Ro ≈ 0, the ratio Qs/Qt increases with h/d50. The suspended load is more than 80% of the total sediment load when h/d50 > 18. The relationship between specific sediment yield, SSY, and watershed area, A, is SSY = 300A-0.24 with an average error of 75%. Besides the specific sediment yield, the mean annual discharge, the normalized flow duration curve, the sediment rating curve, the normalized cumulative distribution curve, and the half yield discharge vary with watershed area. From the normalized flow duration curve at an exceedance probability of 0.1%, small watersheds (A 5000 km2) which have 14 < Q/¯Q < 33. In terms of sediment rating curves, at a given discharge, the sediment load of small watersheds is one order of magnitude higher than for large watersheds. From the normalized cumulative distribution curves, the half yield (50% of the sediment transported) occurs when the discharge is at least 15 times the mean discharge. In comparison, the half yield for large watersheds corresponds to Q/¯Q < 15. The flow duration curve can be parameterized with â and ˆb by using a double logarithmic fit to the flow duration curve. This parametric approach is tested with 35 Korean watersheds and 716 US watersheds. The value of â generally increases with watershed area. The values of ˆb are consistently between 0.5 and 2.5 east of the Mississippi River and the Pacific Northwest. Large variability in ˆb is found in the High Plains and in Southern California, which is attributed to the high flashiness index in these regions. A four-parameter model is defined when combining with the sediment rating curve. The four parameters are: â and ˆb for the flow duration curve, and ā and ¯b for the sediment rating curve. The mean annual discharge ¯Qs is calculated by ¯Qs = āâ¯bΓ(1+ ˆb¯b). The model results are compared to the flow-duration/sediment-rating curve method. The average error of this four-parameter model is only 8.6%. The parameters can also be used to calculate the cumulative distribution curves for discharge and sediment load