Future research may benefit from both analyzing and improving current decision making processes. Photo via Getty Images

Real options represent the decisions companies can make in the face of evolving risk. In the energy and commodity industries, real options are ubiquitous, including the extraction, processing and refinement, storage, and transportation of natural resources. These choices are influenced by ever-changing market and environmental conditions.

Because of these uncertainties, the energy industry has been a key focus of the operations literature on real options. Rice Business professor Nicola Secomandi, along with University of Illinois at Chicago College of Business Administration professor Selvaprabu Nadajarah, were recently invited by the European Journal of Operational Research to conduct a review of the operations literature on real options in energy. Their review included 80 papers across 10 journals active in the field. The research was mostly conducted during Secomandi’s time at the Tepper School of Business at Carnegie Mellon University.

The review examined how often different types of energy and methods of studying related business processes appeared in the operations literature. Nearly a quarter of the papers considered natural gas, more often than any other energy type. Natural gas storage was the most studied process, while the transport and sale of natural gas were less discussed.

While only 10 percent of the papers focused on electricity by itself, mostly in the context of battery management, electricity was discussed alongside emissions and the environment in 22.5 percent of the papers—almost as often as natural gas. About 11 percent of the papers examined both electricity and natural gas.

Roughly 21 percent of the papers focused on crude oil and refined products. Exploration, development, and abandonment of crude oil fields were common topics, while work on crude oil refining and gasoline logistics was rarer.

The review looked at the frequency of use of five categories: real option types, valuation methodologies, model formulations, price risk dynamics, and optimization schemes. Timing options, which irreversibly change the status of an asset when exercised, and switching options, which involve reversible changes, appeared with about equal frequency. Of the valuation methodologies, risk neutral valuation was employed the most often, appearing in nearly 78 percent of the papers. Model formulations were divided mainly between Markov decision processes, which assume that decisions are made at set times, and stochastic optimal control models, which assume that decisions are made continuously. About 63 percent of the papers discussed Markov decision processes, but at nearly 34 percent, stochastic optimal control models haven’t been entirely left out of the literature. Almost 75 percent of the papers formulated models based on spot prices as opposed to futures prices, and over 80 percent adopted normal distribution models, either alone or in combination with other models. Approximate solution approaches dominated within optimization schemes, appearing in 71 percent of the papers.

While several energy sources and analysis tools have been discussed in the literature, the possibilities for future research remain broad. The transition to clean energy sources may increase the complexity of already intricate operations. Its modeling and analysis may require more advanced models than existing ones.

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This article originally ran on Rice Business Wisdom and was based on research from Nicola Secomandi, the Houston Endowment Professor of Management – Operations Management at Jones Graduate School of Business at Rice University.

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Houston researchers develop material to boost AI speed and cut energy use

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A team of researchers at the University of Houston has developed an innovative thin-film material that they believe will make AI devices faster and more energy efficient.

AI data centers consume massive amounts of electricity and use large cooling systems to operate, adding a strain on overall energy consumption.

“AI has made our energy needs explode,” Alamgir Karim, Dow Chair and Welch Foundation Professor at the William A. Brookshire Department of Chemical and Biomolecular Engineering at UH, explained in a news release. “Many AI data centers employ vast cooling systems that consume large amounts of electricity to keep the thousands of servers with integrated circuit chips running optimally at low temperatures to maintain high data processing speed, have shorter response time and extend chip lifetime.”

In a report recently published in ACS Nano, Karim and a team of researchers introduced a specialized two-dimensional thin film dielectric, or electric insulator. The film, which does not store electricity, could be used to replace traditional, heat-generating components in integrated circuit chips, which are essential hardware powering AI.

The thinner film material aims to reduce the significant energy cost and heat produced by the high-performance computing necessary for AI.

Karim and his former doctoral student, Maninderjeet Singh, used Nobel prize-winning organic framework materials to develop the film. Singh, now a postdoctoral researcher at Columbia University, developed the materials during his doctoral training at UH, along with Devin Shaffer, a UH professor of civil engineering, and doctoral student Erin Schroeder.

Their study shows that dielectrics with high permittivity (high-k) store more electrical energy and dissipate more energy as heat than those with low-k materials. Karim focused on low-k materials made from light elements, like carbon, that would allow chips to run cooler and faster.

The team then created new materials with carbon and other light elements, forming covalently bonded sheetlike films with highly porous crystalline structures using a process known as synthetic interfacial polymerization. Then they studied their electronic properties and applications in devices.

According to the report, the film was suitable for high-voltage, high-power devices while maintaining thermal stability at elevated operating temperatures.

“These next-generation materials are expected to boost the performance of AI and conventional electronics devices significantly,” Singh added in the release.

Houston to become 'global leader in brain health' and more innovation news

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Editor's note: The most-read Houston innovation news this month is centered around brain health, from the launch of Project Metis to Rice''s new Amyloid Mechanism and Disease Center. Here are the five most popular InnovationMap stories from December 1-15, 2025:

1. Houston institutions launch Project Metis to position region as global leader in brain health

The Rice Brain Institute, UTMB's Moody Brain Health Institute and Memorial Hermann’s comprehensive neurology care department will lead Project Metis. Photo via Unsplash.

Leaders in Houston's health care and innovation sectors have joined the Center for Houston’s Future to launch an initiative that aims to make the Greater Houston Area "the global leader of brain health." The multi-year Project Metis, named after the Greek goddess of wisdom and deep thought, will be led by the newly formed Rice Brain Institute, The University of Texas Medical Branch's Moody Brain Health Institute and Memorial Hermann’s comprehensive neurology care department. The initiative comes on the heels of Texas voters overwhelmingly approving a ballot measure to launch the $3 billion, state-funded Dementia Prevention and Research Institute of Texas (DPRIT). Continue reading.

2.Rice University researchers unveil new model that could sharpen MRI scans

New findings from a team of Rice University researchers could enhance MRI clarity. Photo via Unsplash.

Researchers at Rice University, in collaboration with Oak Ridge National Laboratory, have developed a new model that could lead to sharper imaging and safer diagnostics using magnetic resonance imaging, or MRI. In a study published in The Journal of Chemical Physics, the team of researchers showed how they used the Fokker-Planck equation to better understand how water molecules respond to contrast agents in a process known as “relaxation.” Continue reading.

3. Rice University launches new center to study roots of Alzheimer’s and Parkinson’s

The new Amyloid Mechanism and Disease Center will serve as the neuroscience branch of Rice’s Brain Institute. Photo via Unsplash.

Rice University has launched its new Amyloid Mechanism and Disease Center, which aims to uncover the molecular origins of Alzheimer’s, Parkinson’s and other amyloid-related diseases. The center will bring together Rice faculty in chemistry, biophysics, cell biology and biochemistry to study how protein aggregates called amyloids form, spread and harm brain cells. It will serve as the neuroscience branch of the Rice Brain Institute, which was also recently established. Continue reading.

4. Baylor center receives $10M NIH grant to continue rare disease research

BCM's Center for Precision Medicine Models has received funding that will allow it to study more complex diseases. Photo via Getty Images

Baylor College of Medicine’s Center for Precision Medicine Models has received a $10 million, five-year grant from the National Institutes of Health that will allow it to continue its work studying rare genetic diseases. The Center for Precision Medicine Models creates customized cell, fly and mouse models that mimic specific genetic variations found in patients, helping scientists to better understand how genetic changes cause disease and explore potential treatments. Continue reading.

5. Luxury transportation startup connects Houston with Austin and San Antonio

Shutto is a new option for Houston commuters. Photo courtesy of Shutto

Houston business and leisure travelers have a luxe new way to hop between Texas cities. Transportation startup Shutto has launched luxury van service connecting San Antonio, Austin, and Houston, offering travelers a comfortable alternative to flying or long-haul rideshare. Continue reading.

Texas falls to bottom of national list for AI-related job openings

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For all the hoopla over AI in the American workforce, Texas’ share of AI-related job openings falls short of every state except Pennsylvania and Florida.

A study by Unit4, a provider of cloud-based enterprise resource planning (ERP) software for businesses, puts Texas at No. 49 among the states with the highest share of AI-focused jobs. Just 9.39 percent of Texas job postings examined by Unit4 mentioned AI.

Behind Texas are No. 49 Pennsylvania (9.24 percent of jobs related to AI) and No. 50 Florida (9.04 percent). One spot ahead of Texas, at No. 47, is California (9.56 percent).

Unit4 notes that Texas’ and Florida’s low rankings show “AI hiring concentration isn’t necessarily tied to population size or GDP.”

“For years, California, Texas, and New York dominated tech hiring, but that’s changing fast. High living costs, remote work culture, and the democratization of AI tools mean smaller states can now compete,” Unit4 spokesperson Mark Baars said in a release.

The No. 1 state is Wyoming, where 20.38 percent of job openings were related to AI. The Cowboy State was followed by Vermont at No. 2 (20.34 percent) and Rhode Island at No. 3 (19.74 percent).

“A company in Wyoming can hire an AI engineer from anywhere, and startups in Vermont can build powerful AI systems without being based in Silicon Valley,” Baars added.

The study analyzed LinkedIn job postings across all 50 states to determine which ones were leading in AI employment. Unit4 came up with percentages by dividing the total number of job postings in a state by the total number of AI-related job postings.

Experts suggest that while states like Texas, California and Florida “have a vast number of total job postings, the sheer volume of non-AI jobs dilutes their AI concentration ratio,” according to Unit4. “Moreover, many major tech firms headquartered in California are outsourcing AI roles to smaller, more affordable markets, creating a redistribution of AI employment opportunities.”