Experiential Learning: The Case of Training MBA Students in an Asian School

Consulting for a startup company is an effective way for Master of Business Administration (MBA) students to learn about management consulting, and the ways and means of a startup company. This paper discusses the experience of an MBA startup project within the context of a core corporate finance course. The project requires the active engagement of several groups of stakeholders—MBA students, the university’s entrepreneurship incubator, a selection of startup companies, and the project’s academic collaborators. In line with the literature, we find that entrepreneurship education through student-startup collaboration contributes to the students’ entrepreneurial learning, and that the offering of an experiential learning course provides students with the opportunities to work with the external business community that yield positive benefits for students, startups, and the university. Our findings add to the experiential learning literature in business education and show that practice-based learning offers an effective learning experience for students whereby all stakeholders are exposed to various communities of practice that facilitate multiple streams of learning. We provide insights on experiential learning from the implementation of a “new” learning pedagogy for MBA students at an Asian institute of higher learning

Know your audience: specializing grounded language models with listener subtraction

Effective communication requires adapting to the idiosyncrasies of each communicative context--such as the common ground shared with each partner. Humans demonstrate this ability to specialize to their audience in many contexts, such as the popular game Dixit. We take inspiration from Dixit to formulate a multi-agent image reference game where a (trained) speaker model is rewarded for describing a target image such that one (pretrained) listener model can correctly identify it among distractors, but another listener cannot. To adapt, the speaker must exploit differences in the knowledge it shares with the different listeners. We show that finetuning an attention-based adapter between a CLIP vision encoder and a large language model in this contrastive, multi-agent setting gives rise to context-dependent natural language specialization from rewards only, without direct supervision. Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the specialization to real-world data. Our experiments demonstrate a method for specializing grounded language models without direct supervision and highlight the interesting research challenges posed by complex multi-agent communication.Comment: 28 pages, 9 figure

Trading Costs on the Stock Exchange of Thailand

This study examines the components of trading costs incurred in trading large and liquid stocks listed on the Stock Exchange of Thailand. We find that aggressive orders pay an immediacy price measured by price impact, whereas executed passive orders gain the immediacy price. We also find a sizable opportunity cost from the unexecuted portion of a limit order that more than offsets the benefit obtained from the partial fulfillment of the order. The total trading cost, which includes price impact and opportunity cost, is positively related to order size and stock price volatility, but negatively associated with firm size, stock price, and stock liquidity. The total trading cost has a U-shaped relation with order aggressiveness. Collectively, our study suggests that, to minimize the total trading cost, the optimal strategy is simply to use a limit order submitted at the best quote.Accepted versio

Know your audience: specializing grounded language models with listener subtraction

Effective communication requires adapting to the idiosyncrasies of each communicative context—such as the common ground shared with each partner. Humans demonstrate this ability to specialize to their audience in many contexts, such as the popular game Dixit. We take inspiration from Dixit to formulate a multiagent image reference game where a (trained) speaker model is rewarded for describing a target image such that one (pretrained) listener model can correctly identify it among distractors, but another listener cannot. To adapt, the speaker must exploit differences in the knowledge it shares with the different listeners. We show that finetuning an attention-based adapter between a CLIP vision encoder and a large language model in this contrastive, multi-agent setting gives rise to context-dependent natural language specialization from rewards only, without direct supervision. Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the specialization to real-world data. Our experiments demonstrate a method for specializing grounded language models without direct supervision and highlight the interesting research challenges posed by complex multi-agent communicatio

2.5D magnetohydrodynamic simulation of the formation and evolution of plasmoids in coronal current sheets

Funding: S.M. would like to acknowledge the financial support provided by the Prime Ministerʼs Research Fellowship of India. A.K.S. acknowledges the ISRO grant DS 2B-13012(2)/26/2022-Sec.2 for the support of his scientific research. D.I.P. gratefully acknowledges support through an Australian Research Council Discovery Project (DP210100709). D.Y. is supported by the National Natural Science Foundation of China (NSFC; grant Nos. 12173012, 12111530078, and 11803005), the Guangdong Natural Science Funds for Distinguished Young Scholar (grant No. 2023B1515020049), the Shenzhen Technology Project (grant No. GXWD20201230155427003-20200804151658001) and the Shenzhen Key Laboratory Launching Project (grant No. ZDSYS20210702140800001).In the present paper, using MPI-AMRVAC, we perform a 2.5D numerical magnetohydrodynamic simulation of the dynamics and associated thermodynamical evolution of an initially force-free Harris current sheet subjected to an external velocity perturbation under the condition of uniform resistivity. The amplitude of the magnetic field is taken to be 10 G, typical of the solar corona. We impose a Gaussian velocity pulse across this current sheet that mimics the interaction of fast magnetoacoustic waves with a current sheet in the corona. This leads to a variety of dynamics and plasma processes in the current sheet, which is initially quasi-static. The initial pulse interacts with the current sheet and splits into a pair of counterpropagating wavefronts, which form a rarefied region that leads to an inflow and a thinning of the current sheet. The thinning results in Petschek-type magnetic reconnection followed by a tearing instability and plasmoid formation. The reconnection outflows containing outward-moving plasmoids have accelerated motions with velocities ranging from 105 to 303 km s−1. The average temperature and density of the plasmoids are found to be 8 MK and twice the background density of the solar corona, respectively. These estimates of the velocity, temperature, and density of the plasmoids are similar to values reported from various solar coronal observations. Therefore, we infer that the external triggering of a quasi-static current sheet by a single-velocity pulse is capable of initiating magnetic reconnection and plasmoid formation in the absence of a localized enhancement of resistivity in the solar corona.Peer reviewe