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The irrationality of the California gubernatorial recall system
No full textWe analyze California’s gubernatorial recall system using basic ideas from the mathematical theory of social choice. We expose some pathologies of the system, use computer simulations to explore how often these pathologies might occur, investigate the effects of strategic voting on the analysis, and suggest changes to the system that cure the pathologies
Feral Horses and Native Ungulates: Evaluating the Potential for Competition in Predator–Prey Communities of Alberta’s Upper Foothills
No full textFeral herbivores are expanding in range and numbers globally, yet their community-level effects in ecosystems are poorly resolved. We evaluated whether feral horses reshape predator–prey–vegetation dynamics in Alberta’s Upper Foothills, Canada, through two pathways; i) potential exploitative competition for forage and ii) apparent competition via shared predators. We combined fecal DNA metabarcoding with remote wildlife cameras to quantify diets, spatiotemporal co-occurrence, and the role of horses in carnivore diet. We analyzed 241 ungulate scats (feral horses, elk, bighorn sheep, cattle, moose, and deer), 96 combined camera-years at 23 sites, and 190 carnivore scats (wolves, cougars, coyotes, bears) from 2023–2025. The diet of feral horses, a bulk-grazing hindgut fermenter, was consistently graminoid-focused (~50% of diet) but broadened to include more forbs in summer (~40%) and shrubs in winter (~41%). Diet overlap was greatest between bulk grazers (horses–cattle) and between mixed feeders (elk–bighorn sheep). Feral horses overlapped with native herbivores on key forage during spring and winter seasonal bottlenecks. Horse and cattle spatiotemporal overlap and convergence on non-native forbs in summer suggests high potential for exploitative competition between non-native herbivores. Camera randomization tests indicated higher-than-expected summer co-occurrence of horses with cattle, moose, and cougars and lower-than-expected co-occurrence with elk, suggesting potential interference competition. Carnivore fecal metabarcoding revealed feral horses as a consistent prey resource. Wolf diet was dominated by deer but included substantial feral horse signal (~25%), coyotes showed broad diets with frequent horse detections (~28%), while cougars had a smaller proportion of horse in their diet (~5%). Grizzly bear samples containing prey DNA were limited (n=9). However, of these samples, feral horses contributed to ~33% of their meat diet. Carnivore diet provides a pragmatic first step for evaluating apparent competition. Together, results show preconditions for both exploitative and apparent competition between horses and native species. We outline decision-relevant next research steps:seasonal/age-specific horse mortality at verified kill sites and updated multi-species density estimates could help parameterize community models to guide adaptive management
SB15-2526: Resolution Regarding Fiscal Policy on the Senate Discretionary Category
No full textSB15-2526: Resolution Regarding Fiscal Policy on the Senate Discretionary Category. This resolution passed a unanimous vote during the February 11, 2026 meeting of the Associated Students of the University of Montana (ASUM)
IMPROVING DEDICATED BIRD RADAR SYSTEMS THROUGH AVIAN TARGET CLASSIFICATION AND BIRD STRIKE RISK ASSESSMENT
No full textMonitoring the migration and movement of bird populations is important for avian conservation. Increasingly, radar systems are being used to understand patterns in movement activity and assess risk of bird strikes with human infrastructure, such as airplanes and wind turbines. While radar systems excel at monitoring bird activity, current systems cannot identify avian targets to finer levels of classification, such as morphological groups or size class, limiting their utility for avian monitoring. Additionally, there are questions surrounding how avian activity is influenced by weather and timing to better inform bird strike risk management at airfields. We worked with radar systems at two US Air Force bases, Ellsworth Air Force Base, SD (EAFB) and Offutt Air Force Base, NE (OAFB) to address these limitations and questions. In Chapter 1, we built machine learning classification models to identify tracks to different morphological and biomass groups of birds using a dataset of tracks identified to species and quantity at each base. We were able to successfully identify unclassified radar tracks to bird morphological groups including songbirds, waterfowl, raptors, gull (at OAFB only) and herons (at EAFB only) using neural networks. We were also able to classify tracks to four levels of track biomass at both bases. Models were base- and equipment-specific, indicating future modeling efforts will require further collection of identified track datasets at a new radar location. In Chapter 2, we built models explaining avian activity as a function of weather and temporal covariates across multiple seasons to understand what factors most influence the intensity of avian activity on an hourly scale, and multiple monitoring scales to see if the effects of covariates on avian activity differed by monitoring scale. Across bases, hour of day, and wind speed and direction played important roles in influencing bird activity, though the strength and direction of effects changed seasonally. We did not find a difference in these effects by monitoring scale at EAFB but did see a difference at OAFB, especially in summer and winter. Our results highlight the importance of building base specific models explaining bird strike risk to aircraft
Revisiting the Secretary Problem
No full textThe venerable Secretary Problem asks how a decision maker (DM) should select one of n candidates, who come up randomly, when the only information available is each candidate’s strict rank within the set of previous applicants. Moreover, DM may select only the current candidate. Our illustrative case has n = 9 candidates, for which the Standard Method is to reject outright the first 3 candidates and then choose the first of the 4th through 8th candidates who is better than all of the first three, or the 9th candidate if none of them is. We compare the Standard Method with two other selection methods that change the conditions under which DM decides:
Reserve Method. Same as the Standard Method, except that the best of the first 3 candidates is held in reserve and chosen if none of the 4th through 8th candidates (Version A) or none of the 4th through 9th candidates (Version B) is better.
Score Method. Each candidate receives a score between 0 and 1; scores are known to be uniformly distributed. In each round, DM decides on a numerical threshold and selects the candidate if she exceeds the threshold. If none does, candidate 9 is selected. We assume that DM chooses thresholds so as to maximize the expected score of the selected candidate.
The Standard Method gives DM a probability of 41% of selecting the best candidate, whereas the Reserve Methods substantially raise this probability to 70% or 74%—depending on whether Version A or B is used—while the Score Method raises it to 55%. Thus, the other methods, especially the Reserve Methods, outperform the Standard Method in selecting the best candidate. But the Score Method requires seeing significantly fewer candidates—an average of 4.3, compared with an average of 6.3 for the Standard Method and 6.3 and 6.7 for the two versions of the Reserve Method.
We also discuss a third criterion, the average rank of the candidate selected, which is about 1.5 for the Reserve Methods and the Score Method but 2.9 for the Standard Method. In sum, the superiority of the alternative methods over the Standard Method reflects the severity of the restrictions placed on the original Secretary Problem, suggesting it is time to revisit the assumptions of this method and consider realistic alternatives
GLI Weekly, February 3, 2026
No full textAdvising Information -- Beyond the Classroom Experience Planning -- Join GLI for Heart Filled Thank-Yous -- Franke Fellow Barrett Clement to give a lecture on Japan --Seniors/Graduating class: Nominate a student speaker -- Calling All GLI Seniors -- We\u27re recruiting a VISTA for the Franke GLI -- Save the Date: 2026 Martin Luth King, Jr. Lecture: Resistance, Resilience, and Radical Love with Dr. Regina Shands Stolfzfus -- Flathead Biological Station Summer 2026 Courses: Application Now Open -- National Student Exchange deadlines -- Spring Study Abroad priority deadline -- Food Systems Panel: Eat with Understanding: A Student\u27s Guide to Food Systems -- Montana DECA State Career Development Conference, Volunteer Judges Needed -- Open Aid Alliance -- VolunteerUM -- Winter Welcome Back -- Foresters Ball -- Mansfield Dialogue: Executive Power and its Limits: Are Constitutional Guardrails Working? Featuring Stephen I. Vladeck on February 10 at 7pm MST -- Branch Center: Upcoming Events and Opportunities -- Scholarship Opportunities -- Bella and Murray Ressler Digital Humanities Fellowship at the United States Holocaust Memorial Museum, AY 2026-2027 -- Experiential Learning Scholarships -- Gilman Scholarshi
Montana Kaimin, February 12, 2026
No full textStudent newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/11192/thumbnail.jp
Redistributing Votes when Candidates Drop Out of an Election
No full textThe U.S. Presidential Primary is a series of statewide primaries and caucuses used by the Democratic and Republican parties to winnow a pool of candidates to arrive at their nominees. As unsuccessful candidates leave the race, there is a question as to how voters who planned to vote for the candidate reassign their votes in the upcoming primaries/caucuses. When a profile of voters’ preferences is independent of elimination under plurality, then voters whose first-place candidate has dropped out of the race have their votes distributed to the remaining candidates in the same proportion of the remaining candidates’ first-place votes. For any number of candidates, we show that this notion of independence can be reframed in terms of a probabilistic view of the election. We relate the repeated dropping out of candidates to Harville’s equations from probability theory. To motivate our analysis, we look at data from the 2024 Republican Presidential Primary