text:
Q: jQuery plugin or technique to partially obscure text fields? What I'd like to do is, as unobtrusively as possible, obscure part of the input on a text field (as though it were a password field) but allow the rest of it to remain plaintext.
It doesn't look like the existing input "masking" plugins do anything but ensure conformance with a provided data structure.
Is there an already-existing plugin, or well-documented technique for achieving this?

A: How about on change, you check to find the string positioning of the character, and replace it with your obscure character? Then write the actual string to a hidden field.
Or simply replace all characters up to a certain length, and run substr on all following strings?

A: I have no idea what it is you're after, but maybe it's something like this:
$('#ElementID').on('keydown keyup', function() {
    var res = this.value,
        len = res.length,
        stars = len>0?len>1?len>2?'***':'**':'*':'',
        result = stars+res.substring(3);
    $("#ElementID2").val(result);
});

FIDDLE
It's just a quick hack, but should give you some ideas?

redpajama_set_name: RedPajamaStackExchange


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According to details, dust-thunderstorm and light rain with gusty winds may occur at a few places of Islamabad, Rawalpindi, Lahore, Gujranwala, Quetta, Zhob, Malakand, Hazara, Mardan, Upper FATA, Gilgit-Baltistan and Kashmir.
Islamabad 8°C, Lahore26°C, Karachi27°C, Peshawar21°C, Quetta and Muzaffarabad13°C, Gilgit 9°C and Murree12°C.


text:
Home » Questions » What Does Ss Mean On A Car?
What Does Ss Mean On A Car?
The SS! As a vehicle fan, those letters inspire awe and enthusiasm. After all, they stand for Super Sport, a package many auto enthusiasts crave.
Let's examine more closely what SS represents on a car. In 1961, Chevrolet released it as an option package for the Impala. Changes were made to the interior and exterior design and the engine. The SS designation was applied to various cars, including the Camaro and the El Camino. It was manufactured until 2018 when Chevrolet chose to cease production.
But what makes SS so special? The Super Sport package was intended to improve the appearance and performance of vehicles. It combined power, design, and technology to produce a unique product. The Impala SS, for instance, featured a high-output engine, a body-hugging suspension, and a stylish design.
It is not surprising that SS has become such a popular product. Ultimately, it provides high-end performance, looks, and terrific value. This is why so many automobile enthusiasts still aspire to buy a classic SS model.
Therefore, if you ever see the designation SS on a vehicle, you will know it stands for Super Sport. This package has existed for decades and continues to be popular among automobile aficionados. Consequently, the SS abbreviation will continue inspiring drivers for years.
What Does "L" Mean On A Car's Gear Shift?
What Does Car Symbol With Key Mean?
What Does D3 Mean In A Car
What Does Train Left Front Tire Mean With a Ford?
How To Remove Fuel Lines From Gas Tank F150?

redpajama_set_name: RedPajamaCommonCrawl


text:
ReviewsBook Review
Paul Tobin: Prepare To Die!
Tasha Robinson
It's an old cartoon and serial-adventure cliché: The villain gets the hero at a disadvantage and cackles "Prepare to die!" It's a meaningless phrase, a stalling tactic designed to heighten the drama and draw out a potentially scary moment for the audience, and give the hero time to turn the tables. Not so in Paul Tobin's enjoyably pulpy debut novel, Prepare To Die!, where a superhero named Reaver takes it at face value. Having lost his hero friends, his latest battle, and his will to keep fighting the good fight on his own, he capitulates to the inevitable. When his nemesis, Octagon, defeats him, holds a weapon to his head, and tosses the hoary old phrase at him, Reaver agrees, and asks for a month to do exactly that—to put his affairs in order before surrendering for execution. Octagon, taken off balance, offers him two weeks.
The rest of the novel unfolds from there, as Tobin alternates between following Reaver—a.k.a. Steve Clarke, reluctant government agent and accidental superhero—as he tries to tie up his loose ends, and flashing back to cover the history that made him so tired of life and ready to let it go. Tobin is an established comics writer; among many other titles, he's scripted Falling Skies and Predators for Dark Horse, worked extensively on the Marvel Adventures line, and collaborated with his wife, Colleen Coover, on the strikingly original graphic novel Gingerbread Girl. Tobin knows hero tropes well—case in point, the titular gimmick that drives the plot. He clearly expects that his audience does, too; much like Austin Grossman with Soon I Will Be Invincible, or A. Lee Martinez with Emperor Mollusk Versus The Sinister Brain, he builds an expansive world of super-powered conflict via a handful of entertaining details, trusting that his readers will find the outlines familiar and will fill in the blanks themselves, which leaves him free to keep things moving quickly. (It helps that the book is set in a familiar modern world where the existence of superheroes is the only major twist; naturally, their lives are captured on YouTube, explored on talk shows, and exploited in unlicensed porn.) Prepare To Die! is a fleet page-turner, whether Tobin is describing action or exploring Reaver's thoroughly damaged inner landscape.
His head often isn't a pleasant place to be. Reaver isn't the brightest or noblest character in his heroic pantheon; preternatural toughness, massive strength, and rapid healing have turned him into a living hammer that sees every problem as a nail that deserves smashing. And the fact that his punches literally take a year off people's lives means he has to seriously consider the weight and potential moral compromise of every blow. He's come to fitfully accept how people see him, and he embraces the role of the vicious, barely controlled thug. And he's well aware of his shortcomings, particularly his lack of wisdom or empathy by comparison with Paladin, his former friend and the greatest of Earth's heroes.
The trend toward superhero novels in pop literature has mostly led to rewarding places; authors working in prose have more room to deconstruct their genre, build their characters inside and out, and delve into more adult material than is necessarily welcome in mainstream comics. Prepare To Die! is no exception, though its take on sex is often juvenile compared to the rest of its content. Reaver is so adult in his experiences and outlook in other arenas, it's embarrassing how often he uses the word "pussy" when recalling his many conquests, superpowered and otherwise, and how trying to actually talk to a woman he cares about turns him into a sheepish, ineffectual dope. The book overall has similar attitude problems with women; far too many of its female characters, major and minor, are characterized largely in terms of exaggerated sexual characteristics, behaviors, powers, or obsessions. In this case, Tobin may have too thoroughly absorbed superhero comics' biggest flaws along with their strengths.
But even at its grimmest or most graphic (and it's both in heaping helpings, since big, dramatic character anguish can be as satisfying a wish-fulfillment fantasy trope as superpowered battles) the book is too much of a slick adventure to take to heart. And the book's central thrust—Reaver's attempts to redeem himself before death, and reconcile the hero he wants to be with the flawed man he's become—makes it easy and natural to excuse his problematic aspects. Like the bulk of the superhero comics it emulates, this isn't undying literature, but it's a thrilling ride, the beach-blanket-book equivalent of a summer cinema blockbuster.


OBSESSED With...The New British Invasion • Toby Blackwell, B.U.P.E.
First new "OBSESSED" of Season 2. Find out what musical group finally got me interested in something in 2012. Also, I check in with previous OBSESSED topics, and I get involved in a celebrity feud.


text:
import time
import pytest
from unittest import mock

import mlflow
from mlflow.exceptions import MlflowException
from mlflow import MlflowClient
from mlflow.utils import _truncate_dict
from mlflow.utils.autologging_utils import MlflowAutologgingQueueingClient
from mlflow.utils.validation import (
    MAX_ENTITY_KEY_LENGTH,
    MAX_PARAM_VAL_LENGTH,
    MAX_TAG_VAL_LENGTH,
    MAX_PARAMS_TAGS_PER_BATCH,
    MAX_METRICS_PER_BATCH,
)

def get_run_data(run_id):
    client = MlflowClient()
    data = client.get_run(run_id).data
    # Ignore tags mlflow logs by default (e.g. "mlflow.user")
    tags = {k: v for k, v in data.tags.items() if not k.startswith("mlflow.")}
    return data.params, data.metrics, tags

def test_client_truncates_param_keys_and_values():
    client = MlflowAutologgingQueueingClient()
    params_to_log = {
        "a" * (MAX_ENTITY_KEY_LENGTH + 5): "b" * (MAX_PARAM_VAL_LENGTH + 5),
        "a" * (MAX_ENTITY_KEY_LENGTH + 50): "b" * (MAX_PARAM_VAL_LENGTH + 50),
    }

    with mlflow.start_run() as run:
        client.log_params(run_id=run.info.run_id, params=params_to_log)
        client.flush()

    run_params = get_run_data(run.info.run_id)[0]
    assert run_params == _truncate_dict(
        params_to_log,
        max_key_length=MAX_ENTITY_KEY_LENGTH,
        max_value_length=MAX_PARAM_VAL_LENGTH,
    )

def test_client_truncates_tag_keys_and_values():
    client = MlflowAutologgingQueueingClient()
    tags_to_log = {
        "a" * (MAX_ENTITY_KEY_LENGTH + 5): "b" * (MAX_PARAM_VAL_LENGTH + 5),
        "c" * (MAX_ENTITY_KEY_LENGTH + 50): "d" * (MAX_PARAM_VAL_LENGTH + 50),
    }

    with mlflow.start_run() as run:
        client.set_tags(run_id=run.info.run_id, tags=tags_to_log)
        client.flush()

    run_tags = get_run_data(run.info.run_id)[2]
    assert run_tags == _truncate_dict(
        tags_to_log,
        max_key_length=MAX_ENTITY_KEY_LENGTH,
        max_value_length=MAX_TAG_VAL_LENGTH,
    )

def test_client_truncates_metric_keys():
    client = MlflowAutologgingQueueingClient()
    metrics_to_log = {
        "a" * (MAX_ENTITY_KEY_LENGTH + 5): 1,
        "b" * (MAX_ENTITY_KEY_LENGTH + 50): 2,
    }

    with mlflow.start_run() as run:
        client.log_metrics(run_id=run.info.run_id, metrics=metrics_to_log)
        client.flush()

    run_metrics = get_run_data(run.info.run_id)[1]
    assert run_metrics == _truncate_dict(metrics_to_log, max_key_length=MAX_ENTITY_KEY_LENGTH)

def test_client_logs_expected_run_data():
    client = MlflowAutologgingQueueingClient()

    params_to_log = {
        "param_key_{}".format(i): "param_val_{}".format(i)
        for i in range((2 * MAX_PARAMS_TAGS_PER_BATCH) + 1)
    }
    tags_to_log = {
        "tag_key_{}".format(i): "tag_val_{}".format(i)
        for i in range((2 * MAX_PARAMS_TAGS_PER_BATCH) + 1)
    }
    metrics_to_log = {"metric_key_{}".format(i): i for i in range((4 * MAX_METRICS_PER_BATCH) + 1)}

    with mlflow.start_run(run_name="my name") as run:
        client.log_params(run_id=run.info.run_id, params=params_to_log)
        client.set_tags(run_id=run.info.run_id, tags=tags_to_log)
        client.log_metrics(run_id=run.info.run_id, metrics=metrics_to_log)
        client.flush()

    run_params, run_metrics, run_tags = get_run_data(run.info.run_id)
    assert run_params == params_to_log
    assert run_metrics == metrics_to_log
    assert run_tags == tags_to_log
    assert run.info.run_name == "my name"

def test_client_logs_metric_steps_correctly():
    client = MlflowAutologgingQueueingClient()

    with mlflow.start_run() as run:
        for step in range(3):
            client.log_metrics(run_id=run.info.run_id, metrics={"a": 1}, step=step)
        client.flush()

    metric_history = MlflowClient().get_metric_history(run_id=run.info.run_id, key="a")
    assert len(metric_history) == 3
    assert [metric.step for metric in metric_history] == list(range(3))

def test_client_run_creation_and_termination_are_successful():
    experiment_name = "test_run_creation_termination"
    MlflowClient().create_experiment(experiment_name)
    experiment_id = MlflowClient().get_experiment_by_name(experiment_name).experiment_id

    client = MlflowAutologgingQueueingClient()
    pending_run_id = client.create_run(experiment_id=experiment_id, start_time=5, tags={"a": "b"})
    client.set_terminated(run_id=pending_run_id, status="FINISHED", end_time=6)
    client.flush()

    runs = mlflow.search_runs(experiment_ids=[experiment_id], output_format="list")
    assert len(runs) == 1
    run = runs[0]
    assert run.info.start_time == 5
    assert run.info.end_time == 6
    assert run.info.status == "FINISHED"
    assert {"a": "b"}.items() <= run.data.tags.items()

def test_client_asynchronous_flush_operates_correctly():
    original_log_batch = MlflowClient().log_batch

    def mock_log_batch(run_id, metrics, params, tags):  # pylint: disable=unused-argument
        # Sleep to simulate a long-running logging operation
        time.sleep(3)
        return original_log_batch(run_id, metrics, params, tags)

    with mock.patch(
        "mlflow.utils.autologging_utils.client.MlflowClient.log_batch"
    ) as log_batch_mock:
        log_batch_mock.side_effect = mock_log_batch

        with mlflow.start_run() as run:
            client = MlflowAutologgingQueueingClient()
            client.log_params(run_id=run.info.run_id, params={"a": "b"})
            run_operations = client.flush(synchronous=False)

            # Parameter data should not be available because the asynchronous logging
            # operation is still inflight
            logged_params_1 = get_run_data(run.info.run_id)[0]
            assert not logged_params_1

            run_operations.await_completion()

            # Parameter data should now be available after waiting for completion of the
            # asynchronous logging operation
            logged_params_2 = get_run_data(run.info.run_id)[0]
            assert logged_params_2 == {"a": "b"}

def test_client_synchronous_flush_operates_correctly():
    original_log_batch = MlflowClient().log_batch

    def mock_log_batch(run_id, metrics, params, tags):  # pylint: disable=unused-argument
        # Sleep to simulate a long-running logging operation
        time.sleep(3)
        return original_log_batch(run_id, metrics, params, tags)

    with mock.patch(
        "mlflow.utils.autologging_utils.client.MlflowClient.log_batch"
    ) as log_batch_mock:
        log_batch_mock.side_effect = mock_log_batch

        with mlflow.start_run() as run:
            client = MlflowAutologgingQueueingClient()
            client.log_params(run_id=run.info.run_id, params={"a": "b"})
            client.flush(synchronous=True)

            # Parameter data should be available after the synchronous flush call returns
            logged_params = get_run_data(run.info.run_id)[0]
            assert logged_params == {"a": "b"}

def test_flush_clears_pending_operations():
    with mock.patch(
        "mlflow.utils.autologging_utils.client.MlflowClient", autospec=True
    ) as mlflow_client_mock:
        client = MlflowAutologgingQueueingClient()

        pending_run_id = client.create_run(experiment_id=5)
        client.log_params(run_id=pending_run_id, params={"a": "b"})
        client.log_metrics(run_id=pending_run_id, metrics={"c": 1})
        client.set_terminated(run_id=pending_run_id, status="FINISHED")
        client.flush()

        logging_call_count_1 = len(mlflow_client_mock.method_calls)
        # Verify that at least 3 calls have been made to MLflow logging APIs as a result
        # of the flush (i.e. log_batch, create_run, and set_terminated)
        assert logging_call_count_1 >= 3

        client.flush()

        logging_call_count_2 = len(mlflow_client_mock.method_calls)
        # Verify that performing a second flush did not result in any additional logging API calls,
        # since no new run content was added prior to the flush
        assert logging_call_count_2 == logging_call_count_1

def test_client_correctly_operates_as_context_manager_for_synchronous_flush():
    params_to_log = {"a": "b"}
    metrics_to_log = {"c": 1}
    tags_to_log = {"d": "e"}

    with mlflow.start_run(), MlflowAutologgingQueueingClient() as client:
        run_id_1 = mlflow.active_run().info.run_id
        client.log_params(run_id_1, params_to_log)
        client.log_metrics(run_id_1, metrics_to_log)
        client.set_tags(run_id_1, tags_to_log)

    run_params_1, run_metrics_1, run_tags_1 = get_run_data(run_id_1)
    assert run_params_1 == params_to_log
    assert run_metrics_1 == metrics_to_log
    assert run_tags_1 == tags_to_log

    exc_to_raise = Exception("test exception")
    with pytest.raises(Exception, match=str(exc_to_raise)) as raised_exc_info:
        with mlflow.start_run(), MlflowAutologgingQueueingClient() as client:
            run_id_2 = mlflow.active_run().info.run_id
            client.log_params(run_id_2, params_to_log)
            client.log_metrics(run_id_2, metrics_to_log)
            client.set_tags(run_id_2, tags_to_log)
            raise exc_to_raise

    assert raised_exc_info.value == exc_to_raise
    # Verify that no run content was logged because the context exited with an exception
    run_params_2, run_metrics_2, run_tags_2 = get_run_data(run_id_2)
    assert not run_params_2
    assert not run_metrics_2
    assert not run_tags_2

def test_logging_failures_are_handled_as_expected():
    experiment_name = "test_run_creation_termination"
    MlflowClient().create_experiment(experiment_name)
    experiment_id = MlflowClient().get_experiment_by_name(experiment_name).experiment_id

    with mock.patch(
        "mlflow.utils.autologging_utils.client.MlflowClient.log_batch"
    ) as log_batch_mock:
        log_batch_mock.side_effect = Exception("Batch logging failed!")

        client = MlflowAutologgingQueueingClient()
        pending_run_id = client.create_run(experiment_id=experiment_id)
        client.log_metrics(run_id=pending_run_id, metrics={"a": 1})
        client.set_terminated(run_id=pending_run_id, status="KILLED")

        with pytest.raises(MlflowException, match="Batch logging failed!") as exc:
            client.flush()

        runs = mlflow.search_runs(experiment_ids=[experiment_id], output_format="list")
        assert len(runs) == 1
        run = runs[0]
        # Verify that metrics are absent due to the failure of batch logging
        assert not run.data.metrics
        # Verify that the run termination operation was still performed successfully
        assert run.info.status == "KILLED"

        assert "Failed to perform one or more operations on the run with ID {run_id}".format(
            run_id=run.info.run_id
        ) in str(exc.value)
        assert "Batch logging failed!" in str(exc.value)

redpajama_set_name: RedPajamaGithub


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There is a special problem, however for ladies when buying for baseball store and especially baseball gloves. Most baseball gear is still manufacturer with males in thoughts, and ladies, whose fingers are a lot smaller, may have a difficult time finding a good match to their gloves.
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text:
Playing down a story for the community good
It's a good story — a great story, as a matter of fact. But would playing it big be responsible journalism?
By Karen Schmidt
Karen Schmidt is assistant regional editor for the Waterbury (CT) Republican-American.
Source: FineLine: The Newsletter On Journalism Ethics, vol. 2, no. 2 (May 1990), p. 3.
On January 30, 1989, two weeks after a gunman killed five children and wounded 29 in a schoolyard in Stockton, California, a letter arrived at an elementary school in Watertown, Connecticut.
"We will start shooting the little pigs on the schoolyard real soon," read the crudely-typed note in part. "We will start with the little sluts so they can't breed. We will not stop until they are all dead."
During my regular rounds covering Watertown the next day for the Waterbury Republican, my usual police department contact showed me the letter and a similar one that had arrived at a resident's home on Saturday. He told me police were stepping up school security, and the school superintendent was sending a letter home that day with every child telling parents about the threats.
I agreed with the cops that this was no kid playing a prank. The ugly tone of the letters seemed to reveal a disturbed adult, vicious toward women and Catholics and very aware of publicity. "We hope to hell you think this is a joke . . . Do not think you can cover this up," it read. "We have started notifing (sic) your people. Thanks for putting their names all over everything."
The other letter and a third that turned up the next day were aimed at a Catholic school down the street. "Dear Catholic Pig: We will start shooting the children on the Magdalen schoolyard real soon — Ladies First," it read.
The question was not whether to cover it, but how. It was an explosive story, and as the only daily reporter regularly covering the town, I was pretty sure I had it first. Everyone in town would know in a few hours once the children brought the superintendent's letter home.
But the thought of a huge outside headline in tomorrow morning's paper made me uneasy. Here was a sick person who craved attention. Media coverage of the Stockton shooting had probably given him the idea. Would the newspaper become an accomplice if we overplayed the story?
How would a splashy story affect the more than 3,000 children?
In addition, as the superintendent pointed out as he urged me to play down the story, he had informed every parent in the district. Everyone who needed to know, knew, he said. In fact, the superintendent's letter was a double-edged sword: Once the story was out, why tread carefully?
I returned to the office and told the state news editor, Charles Dixon. His immediate reaction: Play it small. Get it on the record, but do nothing to encourage hysteria. In a five-minute discussion at the start of a regular planning meeting, he asked the other editors if anyone disagreed with his decision to give the incident the bare minimum of coverage.
They agreed the paper had to mention the incident. But any more than minimum attention could breed panic or, worse, encourage the letter writer.
After considering who could benefit from playing the story big, they decided it was in everyone's best interest to keep it inside.
A short time later, I was on the phone with the superintendent. His relief that we were downplaying the story made me think we must be doing something wrong to make a source so happy. Then he told me WTNH-TV, the local ABC affiliate, had just sent out a crew.
I told Dixon. After just a moment's thought, he said we should stick to our decision. If they were going to be irresponsible, it did not mean we had to be.
I wrote my four-paragraph story. Channel 8's 6 p.m. report did not mention the threats. At about 10 p.m., a friend called to tell me the station was promoting the story for the 11 o'clock news. I told my editor. No change.
The television story made me feel we were doing the right thing. A graphic over the anchor's shoulder blazoned "Watertown Sniper" over a target riddled with bullet holes. The story quoted frightened parents. I was angry at what I considered sensational coverage, and a little deflated: After all, I had gotten the story first, even if it was handed to me.
My story ran quietly the next day. Follow-ups, though written longer and played a little harder, continued in the same tone, dealing mostly with school security and reactions from parents and children.
I never quoted the notes, though other print and broadcast media used some of the milder phrases like "shooting the little pigs." The decision whether to quote the letters was left entirely to me, and in retrospect, I may have been too squeamish. But by the second or third day, the news value would have been all but gone, and the words could renew fear in children reading the paper.
Looking back, I still believe we did the right thing. We can't determine story play only on how many people will read it; we have a responsibility to consider the consequences.
In this case, excessive play could have only bred more hysteria. That hysteria, in turn, would make more news: I could see people pulling their kids out of school for weeks, or demanding heavy security at the schools. In that scenario, the panic does more harm than the original threat.
In a hostage situation, when ethnic hate is involved, or any time news coverage can incite a publicity-hungry person to do more to draw attention, I think we must err on the side of underplaying. Even if the competition gets a flashier story.
The Watertown letter-threat case remains unsolved.


Are we eligible for getting government grant for small business?
What is the procedure to get the government grant?
We are a small business; can we apply to get theMinority Business Grant?
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In Addition to this the Federal Government does not provide any grant for women to start or expand a business.
However there are some special Grants available only to businesses in specific fields or industries identified by the federal or state government as being especially important to the nation or state as a whole, such as medical or scientific research and environmental conservation.
Business in the field of in scientific research and development (R&D) may apply for federal grants under the Small Business Innovation Research (SBIR) program. Similar to SBIR grant state government agencies sometimes offer discretionary incentive grants" to businesses that, in the agencies' opinion, help stimulate the state's or region's economy and advance beneficial causes such as alternative energy development.
People who are searching for possibilities to get this free money must understand that this is Public tax fund which is being awarded to carry a public purpose of simulation. The government therefore follows very stringent compliance and reporting measures to ensure the money is well spent. Government business grants difficult and often expensive to apply for, the governments awarding them typically demand some return on their taxpayers' investment.
Use the Advanced Search tool to search for a grant by eligibility (e.g., for-profits or small business), by issuing agency, or category (e.g., environment or science and technology).
Applying for these grants involves sizeable expenditures of money and time and these grants are awarded on a competitive basis. So it's better to stay away from the frauds that are claiming to get you the grant in exchange of some money.
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THE HAGUE — Among the unfinished business left by the death of Slobodan Milosevic is the central question of whether he was guilty, as charged, of genocide in Bosnia.
But while his death brought a sudden end to his trial at the United Nations war crimes tribunal, the genocide issue may well be decided by another United Nations court based in The Hague: the International Court of Justice.
That court, also known as the World Court, has recently finished nine weeks of hearings on a case filed 13 years ago, in the middle of the Bosnian war. With Muslim [Bosniak] villages under attack and civilians driven into detention camps, Bosnia's lawyers turned to the court, accusing Yugoslavia of violations "on all counts" of the United Nations Convention on Genocide.
The case was held back by a slow-paced institution and by repeated legal moves by Belgrade to block the lawsuit. In the meantime, Yugoslavia became Serbia and Montenegro in 2003, and in May simply Serbia.
Now, the hearings over, judges from 16 countries must give their interpretation of events from the 1992-1995 war. That is expected within the year.
The terror campaign to clear non-Serbs from large swathes of Bosnia has become commonly known as "ethnic cleansing," but the line, if any, between ethnic cleansing and genocide has divided legal experts examining events from Bosnia to Iraq and Darfur.
The World Court suit is unique in that it is not a criminal trial of individuals — like those at the tribunals for Yugoslavia, Rwanda or Sierra Leone — but rather a civil proceeding, in which for the first time one state is suing another charging genocide.
The suit may well be the most complex in the 60-year history of the World Court, the United Nations' highest, which usually deals with issues of sovereignty, diplomatic relations, and land or sea boundaries.
"This will give new relevance to a court that was sidelined by the new war crimes tribunals," said Antoine Garapon, director of the Institute for Advanced Legal Studies in Paris. "It may ultimately pass a historic judgment on Milosevic and his regime, even if that was not its function."
Should the court rule in Bosnia's favor, the Serbian state will suffer the stigma of having committed genocide, an outcome that would implicate the entire Milosevic government.
For Serbian citizens and their fledgling economy, that could mean also being saddled with hefty war reparations. Bosnia has asked the court to award damages for the loss of life and property.
During the war, 100,000 people died, the majority of them Muslims, and entire Muslim towns and villages were destroyed, including their mosques and monuments. No figure was set.
Serbia has argued that there were local excesses of war but no genocidal campaign, that Belgrade did not control events in Bosnia and that a verdict favoring Bosnia will make reconciliation between the neighbors even more difficult.
Bosnia says the opposite, and argues it needs "recognition of Serb guilt" even more than reparations.
Even at this late stage, Serbia has raised new obstacles, challenging once more the jurisdiction of the court. It argued that during the relevant period, Yugoslavia was not a member of the United Nations and, by extension, not a party to the court. In the past, judges twice ruled the court had jurisdiction.
In the recent hearings, the opposing legal teams made ample use of documents, evidence and even witnesses from trials at the war crimes tribunal, which sits a few miles away. The tribunal found genocide at Srebrenica in 1995, when more than 7,000 Muslim men and boys were executed.
But at least one crucial cache of material has remained out of reach. The wartime records of the Supreme Defense Council, which included Yugoslavia's military and political leaders, were handed to the tribunal after much Western pressure.
But Belgrade secured the guarantee that they be accessible only to the court's judges and lawyers. Belgrade officials claimed national security, but some have made it no secret they wanted to keep the records out of the hands of the Bosnians suing them at the World Court.
Tribunal officials familiar with the secret archives said the minutes of the meetings revealed much about how Belgrade ran the war in Bosnia.
"Of course people may say things differently if they know they are recorded," one official said, requesting anonymity because he lacked permission to speak to reporters. "Milosevic was also known to convey important decisions in private." Parts of the minutes were missing, he said.
During Mr. Milosevic's trial, prosecutors said the archives provided the best records and insights into his real power and his state of mind.
Human rights lawyers have insisted that opening the archives is necessary for Serbia to be confronted with its wartime history.
For now, Bosnia must demonstrate that Serbia not only supplied but also controlled the troops that were involved in eradicating Muslim communities — including Bosnian Serb troops and Belgrade's own special forces and militias.
Gen. Ratko Mladic, the Bosnian Serb military commander wanted for genocide by the war crimes tribunal, traveled to Belgrade almost weekly during the war, according to testimony from NATO commanders.
Legally proving genocide requires showing intent to destroy a group, in whole or in part. Phon van den Biesen, of the Bosnian team, said that intent was evident in this case from the pattern of crimes on the ground, including the destruction of close to 1,000 mosques, so evidence from the archives would not be needed.
Lawyers following the case said defining genocide or proving it at this court might be different.
"This is not a criminal trial and the levels of proof needed are not as high," said Richard Dicker, a director of Human Rights Watch. "In any case, it will be hugely important to see how this court interprets and rules on genocide."


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Jörg Breu, 1480 körül – 1537, német festő
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redpajama_set_name: RedPajamaWikipedia


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Podcast, Better Business
Podcast Episode 010: The Science of Decision Making
Decision making is an integral part of effective management. The process of consistently making sound decisions, however, is not intuitive. As human beings, we are subject to many biases that can cause us to mislabel problems, fail to fully identify all relevant information, and ultimately draw misinformed conclusions.
In this conversation Rick and fellow podcaster, Brandon Laws of the HR for Small Business Podcast interview Pat McCauley, Owner and President of Bridgewell Agribusiness LLC (BWAB). Prior to BWAB, Pat spent 22 years at Susquehanna International Group (SIG), a global quantitative financial trading firm based in Philadelphia, Pennsylvania. At SIG, Pat held a number of key positions including COO, Head of Business Development, Head of Equity Trading and Head of Trader Development and Education. He developed much of his material for Decision Science at Susquehanna and has applied it successfully since then, both in his businesses and for business audiences in seminars and workshops.
This engaging conversation covers some of the framework he has developed to help managers make better decisions, as well as generate awareness and strategies to avoid some very common traps and biases that cloud effective thinking.
The book mentioned in the podcast, Thinking in Bets: Making Smarter Decisions When You Don't Have All The Facts by Annie Duke can be found here.
Music by Mironov Sound
Decision Science Seminar
Pilot Wealth and Xenium HR will be co-hosting the seminar, Traps and Biases – How to Make More Effective Management Decisions, with Pat McCauley on June 12, 2018. Seminar fee is $49 however for podcast listeners only, the first dozen registrants will receive a 50% off discount. Use the promo code "PILOT" upon registration to apply the discount. Registration can be found here.
Interview transcript:
Rick Thomas: Very cool to be here with Brandon and Pat this morning. Brandon and I are doing this in kind of an experiment here. We're doing a simul-podcast. So we will see how this works.
Brandon: It's exciting.
Rick: So very cool to be teaming up and doing this with you.
Brandon: Yeah, I'm excited to have Pat here too.
Rick: Even more so, yeah. So we've got Pat McCauley, CEO and owner of Bridgewell Agri Business here in the area, in the Portland area. Welcome, Pat.
Pat McCauley: Thanks. I appreciate you having me on.
Rick: We had the opportunity to meet Pat a few months ago, actually late last year.
Rick: He was a guest on a panel that we had, as part of the Leadership and Economic Summit that Pilot and Xenium has done for a number of years here in the Portland area. As part of the CEO panel, you participated and in getting to know to panelist, Pat began to talk about decision science. Right away, I started geeking out because I'm really curious.
First of all, when I meet people that are a lot smarter than me, I pay attention. So definitely wanted to learn more –
Pat: I appreciate that.
Rick: Learn more about Pat and rather than listen to me ramble on, tell us a bit about Bridgewell Agri Business. What is it do you guys do? Then kind of what has been your larger career track? How did you get to that place? What kind of informed this whole topic around decision science?
Pat: Sure, thanks. Bridgewell Agri Business is in the business of sales and trading of commodity-based food products. So we trade grains, oils, some specialty fruits, tropical products.
Our business is national and international. We focus primarily on customers that are looking to solve problems. So we tend to focus on products where customers have either historically or currently have issues with getting supply of different commodity products that they need and our job is to help them figure out how to solve those problems.
So what we end up doing is we talk about ourselves as supply chain solutions in niche markets. So we tend to manage the overall supply chain. We find the source of the commodity. We manage the supply chain together to the customer. We do some value-add in getting the customer exactly what they need.
So sometimes we're – for example, we might be refining oils. We might be sourcing organic oils. We might be finding specialty grains and then having them floured or milled to a certain specification.
What really got me to the business was I was recruited to be the CEO of a different – of a related company about four years ago and that job kind of morphed into me actually buying the Bridgewell Agri Business from that entity and going out on our own. I have two partners. We work in the business together.
Prior to that and what really kind of shaped my background, particularly with decision science, was my experience at Susquehanna International Group, which is a large Wall Street proprietary trading firm, one of the largest proprietary trading firms in the world at this point. Just to give some perspective, they probably do north of 15 percent of the average daily volume in derivatives markets, so options and ETF trading and they do about – probably over two percent of the listed NYSE and Nasdaq stock volume on a daily basis as well and all the capital is just from the partners who started the business in 1987.
Rick: In terms of dollars that are being traded, give us some sense, just to put scale to that.
Pat: It will be billions of dollars in long and short positions on a daily basis that are rolling over.
Rick: Yeah.
Pat: Now not all the positions are long term positions. It's typically short term. But I was fortunate. I started there out of business school in 1991. I was hired as a trader. I started in Chicago on the floor of the Chicago Board Options Exchange and there were about 125 people when I started.
I found myself in1996 – they asked to be the COO of the company. At that point, we had about 350 people. I had two different stints as a COO and after the first one, which was about seven years, we went from 350 to about 1250 people.
I was responsible for a lot of the growth and development and the hiring and the decision making around what we were going to do in terms of what businesses we were going to be in and how we were going to staff those businesses.
Rick: Right, right. In particular to the decision making, I'm assuming that was kind of what – what was that, that road that led you to this whole notion around decision science?
Brandon: I was hired originally as a trader. But very quickly, as the company was growing, they approached me and they said, "Would you be interested in actually teaching other people how to trade?" We were hiring people like crazy and we did not have anybody who was formally working on teaching other people, who we were hiring, how to make the trading decisions.
So I spent – with one of the managing directors, one of the founders, I built the curriculum which they still use today, to teach everybody how to make trading decisions.
It was in that that I realized that I had a knack for teaching. I had a real interest in decision science among other things and I put together that curriculum. So I would say that was probably the seminal time where the things that I do today kind of came out of that experience of being – you know, effectively the teacher of all the people that worked at Susquehanna who were trading in venture capital.
Rick: Interesting, interesting.
Pat: And we're going to talk – I mean I know we're really going to talk about trading and decision making and so I'm anxious to dive into that.
Rick: Right.
Brandon: Yeah.
Pat: I don't want to sort of spoil the party.
Rick: Yeah. Well, let's get there quickly and I'm going to mention one more thing for the listeners, that if this topic is intriguing, stay tuned, because you can hear some really cool stuff. At the end we're going to talk about an opportunity to participate in the seminar that we're going to host between Xenium and Pilot jointly here in Portland in June, in which Pat is going to lead for us. So stay tuned for that.
Rick: So let's get to the goods as they say. Brandon, why don't you tee us off?
Brandon: Yeah, so I'm really – I mean I'm fascinated by decision making in general and obviously there's a science behind it. Our brains are very complex and I think there's so much we don't obviously know about how we make decisions and things like that. Your background on trading is fascinating and you have some – your component to your work is thinking like a trader in terms of making the right decisions. Talk to me about that. Kind of unpack that thought there.
Pat: The real premise for trading is trading is really decision making under uncertainty. So you are forced to make a lot of decisions in a very compressed time period where you get very quick feedback on the decisions that you made and to the extent that you are risking a significant amount of capital in making those decisions. You get flushed out pretty quickly in terms of whether or not you are a good decision maker or not a good decision maker or if you have biases or you have other – you know, blind spots in your thinking.
So trading was always really, really good for teaching people how to – it's a good form for teaching people how to make decisions.
Brandon: Because things are moving so fast.
Pat: Because things move so quickly and because – and this is true in the real world, which I don't consider trading to be the real world. In the real world, it's true because you have to make decisions with incomplete information. So imagine you are – you could think of yourself as a manager of a company and you're running a large business and you're trying to make decisions about what to do in the future. Compare that to somebody who's standing in a trading ground and they're trying to make a decision about whether or not they want to risk millions of dollars by being long or short in any particular security that they're trading at that time.
They're going to know within a very short period of time, probably minutes, whether or not their decision was any good.
Brandon: Well it's funny. I always think back to the book Blink by Malcolm Gladwell. It's like kind of a – the thesis behind the book is you make split-second decisions and sometimes they're right and sometimes they're wrong. I imagine in the trading world, things are moving so fast and there are high stakes. A quick bad decision can cost a lot of money.
Pat: Yeah. It can cost you a lot of money really quickly and it's pretty significant. So one story we tell is the first week that I was at Susquehanna – and I was fresh out of business school, which of course meant that I thought I knew everything. I was down on the trading floor and we were standing around as a group and we had started – started to have a conversation about basketball. Now I played basketball in college and I've always considered myself to be a really good basketball player.
Suddenly the conversation as traders will have it gravitate to it. It sort of gravitated to gambling and betting and me being so cocksure about who I was and what I knew. I was kind of speaking up about wanting to be involved in the bet and people were sort of aggressively trying to bet against me. I realized something didn't feel right. So my instinct was, you know, maybe I'm sort of leaving out what the bet was.
But what the – maybe I'm not really thinking about this the right way and I sort of quickly came to realize, mostly through people kind of verbally hitting me over the head, that there was something that I was missing about what was going on in the bet and that I was really just a sucker.
It was kind of a – it was a very good moment, very early in – like literally in the first week of my career where I was like, "OK. Well, maybe I don't know as much as I thought and this is a little bit humiliating," and everybody sort of dispersed and walked away and my manager at the time who was a phenomenal trader and a great guy, he looked at me and he said, "Don't ever say anything that you're not willing to bet on."
So you start talking – I had started to talk about what I was willing to do and what I thought I could do. He said, "Don't say anything you're not willing to bet on." That's really kind of the essence of what trading is, is if you have opinions about things and you have decisions that you want to execute on or you want to risk capital on, you're really just taking a bet and you better be really confident that the return that you're expecting to get is compensating you for the risk that you're taking. Too often it happens in business where people don't really think about things in that way. So there's a great book that just came out by a woman named Annie Duke who was a professional poker player.
I'm fortunate to actually know her personally. She's a great person and she just came out with a book called Thinking in Bets, which really kind of talks about – it's the first book that I've read where it really talks about kind of the trader mentality and how you might apply it to other things in business. But if you think about it, if trading is really decision making under uncertainty and you're making decisions about taking risk, with incomplete information all the time.
Trading is really no different than running a business or managing people or thinking about what you want to do with your company. So some examples. Hiring and firing. That's really – you're making decisions about who to keep on your team and who not to have on your team, with most of the time not full information.
Pat: Forecasting and budgeting. Capital allocation, new business development. These are all arenas where you are making bets about the future of your business with not full information. One thing that's really important is if you had full information, there's no decision to make and I think people forget that very often, that you just – if you know for sure that something is going to happen, then we don't have to have a discussion about making a decision. It's already done. We know what the outcome is going to be.
But that's where it's really fun is when you're not really sure and you have to make these decisions. If you do it carefully and you do it thoughtfully and you keep score with what you do – and we will talk more about that later – you can be a really effective decision maker in what I would say is thinking more like a trader.
Rick: Well, that's great. So let's continue to pull that thread. You talk about probabilistic thinking in the materials that we've reviewed and what not. First of all, define that. What does that mean?
Pat: The first thing I would say is there are really kind of two key elements to being a decision maker. One is truth-seeking and one is probabilistic thinking. So it's really about – truth-seeking is really about honesty and candor. So honesty with yourself, honesty with the people that work for you, candid feedback that the market gives you. Now in the trading world, the feedback is as candid as it gets and it is just black and white. You either win or lose and it's not so easy and it's much more subtle obviously when you're managing people and you're managing business, but honesty and candor.
Then probabilistic thinking is – I think is really around inquiry and curiosity. So if you are thinking in probabilities, you're thinking about not just what you think is going to happen, but how likely is that to happen. What are the other things that could happen and really how wrong could you be about what it is that you're thinking?
Pat: So probabilistic thinking is really about framing all of your decisions with what do I expect to happen and then how likely is that outcome to come to be. What are all the possible other outcomes? Then from a risk-reward standpoint, I now have a framework for evaluating what I expect to happen and what the risk associated with the outcome, the variance of the outcome is.
That's I think applicable to really anything that you do. It's particularly applicable to trading because trading is bound by mathematics to a large degree. So it makes it simpler. But really if you think about it, anything that you do in business, whether you frame it this way or not, you have some idea of what you expect to happen and you have some sense that what I expect to happen might not happen and I will think about maybe other things that could happen. But how rigorously am I doing that? How scientifically am I doing that? That's really what the basis of – the fundamental basis of probabilistic thinking is.
Rick: And I'm curious. For the people that have excelled in being able to create that mental model or that framework that really serves that probabilistic mindset, it seems like there's qualitative aspect to people that do it well and people that don't do it so well. I'm guessing here – you know, in dealing with the implicit uncertainty that comes with most – any decision, there's that emotional component. Does the framework of probabilistic thinking help mitigate that emotional component or is that just a qualitative aspect that – you know, it's either a virtue that people have, that they're able to quiet or it's – or not and they will never get it.
Pat: Well, I would say that anyone is capable of getting it. So the first thing I would say is anybody can learn how to think probabilistically, even if you're not a math person. So what I hear a lot of times when I talk to people is "I don't like math," or "I was never very good at math," or "I'm scared of math."
What I realized mostly through trial and error with people that I've worked with and people that have worked for me is you can explain to them that, well, there's actually a mathematical component to really anything that you do.
If you work in human resources or you work in accounting, obviously it's math. If you work in operations, you work in technology, there's a math component to all those jobs. I think people get very nervous when they hear the word "probabilistic" because it's like, "Well, I'm not really sure. I'm not very good at math. I don't know what that really means. I'm not …"
But if you just – if you think about it in terms of – almost every decision that you make, whether it's personal or professional, you are basically – and Annie Duke says this in her book is you are – when you make a decision about something to do, you are in some respect betting against all the other possible outcomes that you could have chosen.
Pat: Or all the other possible versions of what the future might look like or what you might look like in the future. So you're doing this subconsciously. I would encourage people to think about doing it a little bit more rigorously, so that you can do it better. So when we talk about – when I think about probabilistic thinking and I think about I have some idea of what I expect to happen and then I have some sense of how likely that is to happen, the way I think about that is I'm thinking about kind of confidence intervals around my assessments.
So I might think that something is going to happen. I have to have some marker or some confidence interval around how likely that is. So a good example is in the stock market, this happens all the time, is if I want to buy – we will use Facebook because they're in the news a lot. Everybody knows Facebook.
Rick: They're very topical.
Pat: You can go in your computer right now and you can see what the current price of Facebook is. But what you're not seeing or what you can see around that is what's the market for Facebook. What's the bid and what's the ask? How much volume is the – in aggregate is the world willing to buy on the bid or willing to sell on the ask before the price changes?
So what that really is in a very simple sense and a good example is that's a confidence interval. So people think they know what Facebook is worth today based on all the information they have at this particular moment and here's their confidence interval around it. It's marked by the width and the size of the market. That's really what we're talking about doing when we talk about probabilistic thinking is thinking about your decisions in terms of what you expect to happen, what the current price is based on what you know and then a confidence interval with width and size. How wide and how deep is the market that you're willing to make around it?
Obviously the more confident you are, the tighter you can make your market and the more you might be willing to bet on that outcome. I think that's a good analogy, I think, for thinking about how you might make business decisions or how I might make business decisions that have nothing to do with trading.
Brandon: What if we had taken so much information, we're really confident in what we're thinking about, and then all of a sudden, we develop these biases? Like for example, a confirmation bias. I hear this a lot where you become so confident in a way of thinking and you – all you're doing is going out and seeking more information to support that, maybe not intentionally. But how does the biases play a role in how we make decisions?
Pat: That's a very good question. I think that's – I don't want to give away too much with the seminar. One of the things that we will cover in the seminar in a fair amount of detail and I think in ways that people will be able to personalize is talking about confidence and over-confidence in particular.
So without giving away too much in the context of confidence is – you know, why are weather forecasters really good predictors and doctors are not? You would hope – no offense to weather forecasters – the doctors who have had much more extensive education and presumably have gone to – have demonstrated a high level of aptitude for school, are going to be smarter than weather forecasters.
Brandon: It's all about the information.
Pat: At least in terms of academics. Yet weather forecasters are really, really good at making predictions relative to – it turns out weather forecasters are actually not very good predictors of the weather. It's a separate issue. Nate Silver covers that. Nate Silver who is –
Brandon: Yeah, he has got great work.
Pat: He does great work. The Signal and the Noise is a great book. Nate Silver has sort of kind of blown the doors off the myth that weather forecasters are actually good predictors. They're not. But my point is, is that relative to doctors, they are or historically have been. So why?
The answer is that if you're a weather forecaster, your job depends on your ability to make predictions about what's going to happen with the weather, at least out – hopefully out at least 48 to 72 hours. But the point is, is that you get a lot of feedback. So you make a lot of decisions. You have a lot of modeling tools. You get a lot of feedback. When you don't do a good job consistently, you get fired or you get replaced because you're in the – if you're telling people it's going to snow or it's going to rain, take your umbrella, and it's really hot and sunny, and they're wearing galoshes and it's – you know, they should be wearing sandals, they're not going to be too happy with you. They're going to call the station and you're going to get fired. Whereas if you're a doctor and you go in and you do an examination on Rick and the doctor says, "Rick, I think you may have some really exotic disease. It gives you about a two percent chance of survival beyond six months. But I'm not really sure. Let me do some more tests and get back to you," there's no recourse.
So they don't keep score and there's no recourse if their decisions are necessarily bad. That's not a bad thing. I don't mean that as a value judgment. But the point is, is that they don't have any mechanisms that are set up for them to get really good feedback on their assessments of what might be wrong with Rick when he comes in and says, "I don't feel good."
That's really what – so what you want to do is you want to have your confidence in your assessment of anything match what the actual likelihood of that thing happening is. So someone who's really good – someone who has done really good work on this is a gentleman named Phil Tetlock and he has a book called Superforecasting. He has a book called The Halo Effect. If you go online and you Google his name and you Google a confidence test, he actually has an online test where you can measure your – how confident you are with the actual – how good you are sort of measuring your confidence with your actual predictive abilities.
What his research has found, which is really true, is that people are not very good at predicting and they don't really measure and they don't really care and we will get to biases later where they tell a story around that, that makes them feel better about themselves. But his point is that if you want to be really good at forecasting, you want to be really good at predicting, which is really if you want to be really good at probabilistic thing, you need to be able to match up your level of confidence with your predictive ability.
So it's not about you being 100 percent right all the time. But if you're only 60 percent right, you better be – have a 60 percent confidence interval around that and you better prepare for the other 40 percent.
Rick: Right. What's the downside –?
Pat: But if your outcome is 60 percent to be true and you're 100 percent sure, you're setting yourself up for making really, really bad decisions.
Rick: What we've been mainly talking about are – are how to prepare for or how to deal with decision making in the moment. What about after the fact? What are kind of some guidelines in terms of assessing the quality of the decision making or how we want – how do you look at that?
Pat: I think you have to really do two things. One is you have to force yourself and force the people that you work with to make some assessment of how confident they are in what they're assessing. Then you have to keep score. So oftentimes, we don't do confidence intervals. We don't keep score. But then we get outcomes regardless of whether or not we do the first two things.
Pat: And then we tell ourselves a narrative around those outcomes, that speak to I think on a lot of levels, the biases that people are subjected to. Let's do an example. So let's talk about financial modeling. Every company does financial modeling. I do it all the time. I literally do it on a daily basis. I've been trying to predict what our cash flow and our cash position is going to be on a daily basis.
So I'm kind of fully engaged in it. But people do financial modeling all the time. So they do financial modeling around what they expect salespeople to generate, what they expect their company to do on profitability on a daily, weekly, monthly basis depending on – or yearly basis, depending on their business. We're really just in the business of doing predictions and we just talked about the fact that we're not particularly good or wired to be good at doing predictions.
So if we take financial modeling as the example, what a model really is, is a model is a representation of what you expect the world to look like or hope the world is going to look like, right?
So what do you do when you create a model? You come up with baseline based on a bunch of assumptions. So any model that you create is really only as good as the assumptions that you make around that model. What we don't do a lot of times is we don't rigorously test our assumptions. We don't build confidence intervals around our assumptions. We don't think about how likely our assumptions are to be wrong until exposed, we see what the outcome is. So if we're going to be really good at making decisions and doing predictions around things in the context of modeling, we better be really good at – before we come up with what the model looks like is rigorously evaluating our assumptions. So we know where our model could break down and we have an appropriate level of confidence in our model.
It doesn't mean that we're going to walk out the door and say, "I'm 100 percent sure that what I have on this paper is going to happen." But I want to make sure that my confidence in what's going to happen matches up with what's on the paper.
Pat: And that's really, really difficult to do because we're not in the business of making assumptions or testing our assumptions. We don't really have a good understanding overall of risk or how to measure risk or how to evaluate risk. We don't really do a lot of post hoc analysis over what happens. We look at the outcome. But we don't necessarily spend a lot of time thinking about what the outcome – how likely that outcome was to happen before it actually happens, right?
We're much more reactive when we could be a lot more proactive in making those kinds of decisions. You brought up something, which we're going to now kind of get into is biases. So when you brought up the word "emotion," a lot of times what emotion really talks to is the biases that we all have.
When I say biases, what I mean is we are all subject to – as human beings, our brains are not hardwired to make rational, sound, fundamentally-objective decisions. We get fooled all the time by our own intelligence and that's really what biases talk about.
So biases are not about – they're not value judgments. They are things that even people who are really, really good at making decisions are subject to these biases all the time and even if you're aware of them, you're always subject to them.
So it's not a – I've gone through – you know, instead of AA, I've gone through BA, right? Bias Anonymous and now I'm cure. I've gone through the 12-step program.
There is no cure for it. I think what you can hope for at best is to have a really functioning awareness of what these biases are and then you can be really, really successful at consistently making really good decisions.
I'm aware of it all the time. I spend a lot of time thinking about it and I make – I make mistakes all the time. I get clouded by – my judgment gets clouded all the time in very small and subtle ways, probably a lot less than maybe other people would. But clouded nonetheless.
Brandon: Bringing science into this discussion makes so much sense because you hypothesize. You test that hypothesis and then you sort of recap and you look at, "Hey, did it go exactly the way we thought?" and then –
Rick: Objective analysis.
Brandon: Objective analysis. I don't think we do that in decision making.
Pat: We don't and that's one of sort of – that's one of the big issues that I think if you – and again, this is always post hoc. But when you think about why did – why did we not hit our budget last month or why did we not make the revenue we thought we were going to make last year? Why didn't the salesperson who we hired work out the way we thought they were going to work out when we hired them?
The answer a lot of times is that it is not that we were wrong or we got bad luck. It has a lot more to do with maybe we weren't really focused on the right things upfront. Maybe we made assumptions that we shouldn't have made. But probably as much as that is we're probably just too confident in what we thought going in. That's a big problem, right? Because if you have people working for you and you say, "Here's your budget. I expect you to hit your budget," well, sometimes people hit their budget and sometimes they blow through their budget, right? Sometimes they miss their budget.
There's variance there and you need to understand – you need to have a sense of how confident you are in what you originally thought the budget should be, which means you got to really stress-test your assumptions around the model that you give somebody because that's really just giving them a model and saying, "Here's what I expect you to do and why."
But without speaking to how much variance there is around that or what risks there are that may or may not be in the person's control, you're setting them up for either failure or you're setting them up for an interpretation post hoc that is probably biased.
Rick: For those of you listening, you're probably getting a sense of how much meat is that – there is on this bone and I could go for a lot longer. But I think we're probably going to summarize it at this point. But definitely let's talk about the event that we're having on June 12th. So we – Pat, we're going to have you come in and do a – kind of a half-morning seminar on taking a deeper dive on unpacking some of these topics as well as that – you've talked about today as well as a few others and along with some other people in the room to go through maybe some live-fire examples. Is there anything else you would want to tease up on that seminar at this point?
Pat: Well, there's a lot of tease up. I mean we talked about overconfidence. There are a number of other biases. Warren Buffett is quoted as saying, "We interpret all information so our prior conclusions remain intact." I think that's a really good way of summing up a lot of the biases.
So some of the things that we will talk about in a fair amount of detail and give some tools and skills that people can take away to – again, to be aware of these biases. It's overconfidence. It's confirmation bias, which is really about looking for information that confirms what we might already believe.
There are a number of self-serving biases that we will talk about. Self-serving biases are really around stories that we tell ourselves, so we can preserve our beliefs and our self-esteem or what we thought was true. There are hindsight biases. We will talk about hindsight bias, which is really – you know, we view the outcome and then we construct a story around what we believe happened and why it happened.
You know, the famous quote is, "I knew it all along."
Brandon: Yeah, right.
Pat: That's really what hindsight bias talks about. There are some other effects too like the "Lake Wobegon Effect," which is this – Garrison Keillor who is a writer and satirist and is a radio personality, he created this fictitious town in Minnesota called "Lake Wobegon" where everybody who lives there is above average.
Brandon: That's not possible
Pat: It's this mythical sort of place that everybody should – every town should strive to be like Lake Wobegon and everybody is above average. But let me just throw out a couple of quick statistics, which I think – this is why it's called the "Lake Wobegon Effect" is 25 percent of all employees think they're in the top one percent of performance, which –
Brandon: The math doesn't work out.
Pat: Eighty percent, eighty percent – and these are all studies that have been done. So I'm just kind of throwing out the summaries.
Pat: Eighty percent of school children rate themselves above average in leadership. So 80 percent of the kids think they're in the top 50 percent.
Brandon: Eighty percent, wow.
Pat: This one is my favorite one. Ninety-four percent of academics rate themselves above average, relative to their peers.
Brandon: That's unreal.
Pat: Yeah. In terms of – I believe it's in terms of research and the work that they produce. Here's another interesting one just – again, just as a teaser is, you know, 75 percent of employees score themselves higher than their peers and their managers do, than their managers score them, on the same – in evaluations.
So when you do like 180-360 kind of peer reviews. So when you think about sort of all these things going on and you say – and here's the problem when you're a manager is I can say, "OK. I'm a manager. I'm aware of this stuff and I'm aware of it as anybody. I know that I'm still subjected to it. So I worry about people who don't think about it as much as I do."
But then consider this on top of it is you have a whole bunch of people that work for you and they don't know anything about this or they don't pay attention to it or you don't evaluate them on it or you're not teaching them that. So they are going to be really, really subject to these biases.
Pat: And that's really what I think – if I was going to say, "What's their summary takeaway that we would hope?" is I would hope that people would leave and say, "I have an appreciation for what these are and I have some tools that can help me at least do a better job of managing myself and helping to manage the people who work for me."
Rick: Well, we will definitely include more information along with the podcast post on how to – how to sign up for the workshop.
Brandon: It's pretty limited, 60 spots.
Rick: Yeah, about 60 spots. Yeah, yeah.
Brandon: So podcast listeners really get an exclusive look at this –
Rick: Definitely, definitely. So more information to come. Definitely look for that. Pat, this is awesome as always. I always enjoy diving into this and look forward to doing this more on the 12th. Thanks Brandon.
Brandon: You're welcome.
Rick: This was really cool doing this.
Brandon: Thank you guys. I really appreciate you doing this and it's great to get the word out on decision making.
Rick: Very cool. We will do it again,
Brandon: Thanks Pat, Rick.
Pat: Thank you.
[End of transcript]
Tagged: #decision science, #decision making, #bias
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FALL FAIR: | Exhibitors are being asked to give a hand up to Kuu-us Crisis Society.
The Alberni District Fall Fair is four days of fun and activities highlighting myriad factions of the community. But there is also a fundraising aspect to the fair.
This year, the fair hopes to collect much-needed toiletry items for Kuu-us Crisis Services.
"Kuu-us tells us they are in desperate need of toiletry items in particular, so we are asking all of our exhibitors to consider donating an item," Fall Fair administrator Ann Siddall said.
This is the first year they are asking their exhibitors to get in on the fundraising. Siddall said they typically get 300 people exhibiting everything from livestock to handmade, homemade and homegrown items.
Items can be dropped off in the two giant red baskets at the fair office when exhibitors pick up their exhibitor tags. Their names will then be put into a draw for a $50 gift certificate for Boston Pizza.
The Fall Fair also collects food for the food bank. Anyone bringing a non-perishable food donation to the gate for the first hour of the fair on Thursday, Sept. 10 will get in for free. "Last year we raised more than one tonne of non-perishable items," Siddall said.
For more info about the Fall Fair, please go online to albernifair.com or a special wrap in the Thursday, Sept. 10 issue of the Alberni Valley News.


text:
R.U.R (Rossum's Universal Robots)
The play begins in a factory that makes artificial people, called roboti (robots), from synthetic organic matter. They are not exactly robots by the current definition of the term: they are living flesh and blood creatures.
Play title: R.U.R. (Rossum's Universal Robots)
Author (s): Karel Capek, Paul Selver, Nigel Playfair, Alan William Landes
Publisher: Penguin Books, Aventinum
Publication Date: 2004 (Penguin), 1920 (Aventinum)
Primary Discipline: Genetics
Secondary Discipline: Technology
Scientist (s): Fictional
Source Texts: Unknown
Character Breakdown: Humans: 6m, 2f;
Harry Domin (Domain): General Manager, R.U.R.
Fabry: Chief Engineer, R.U.R.
Dr. Gall: Head of the Physiological Department, R.U.R.
Dr. Hellman (Hallemeier): Psychologist-in-Chief
Jacob Berman (Busman): Managing Director, R.U.R.
Alquist: Clerk of the Works, R.U.R.
Helena Glory: President of the Humanity League, daughter of President Glory
Emma (Nana): Helena's maid
Robots: 4m, 2f;
Marius, a robot
Sulla, a robotess
Radius, a robot
Primus, a robot
Helena, a robotess
Daemon (Damon), a robot
Setting: A factory
Time Period: 1920, then several years after
Synopsis of Play: The play begins in a factory that makes artificial people, called roboti (robots), from synthetic organic matter. They are not exactly robots by the current definition of the term: they are living flesh and blood creatures rather than machinery and are closer to the modern idea of clones or replicants. They may be mistaken for humans and can think for themselves. They seem happy to work for humans at first, but a robot rebellion leads to the extinction of the human race.
First Performance Date: January 25th, 1921
First Producer: National Theater, Prague
Performance History: R.U.R. (Rossum's Universal Robots) had its first performance on January 25th, 1921 at the National Theater in Prague. The play introduced the word "robot" and was the first piece of science fiction ever broadcast on television. It was made into a 35 minute, TV adaptation on the BBC in 1938. https://www.tor.com/2012/10/30/sff-onstage-rossums-universal-robots-rur/
Review Links:
http://www.chicagotribune.com/entertainment/ct-ott-0920-on-the-fringe-20130918-story.html
https://mdtheatreguide.com/2013/07/fringe-review-r-u-r/
https://www.theatreview.org.nz/reviews/review.php?id=8810
https://www.irishtimes.com/culture/stage/theatre/r-u-r-review-may-the-robots-rise-up-to-meet-you-1.3198373
https://www.nytimes.com/2016/06/09/theater/review-universal-robots-a-sci-fi-love-story-in-slo-mo.html
http://chicagotheaterbeat.com/2013/09/16/review-r-u-r-rossums-universal-robots-strangeloop-theatre/
Production Photos:
1928-1929, Theatre Guild 1928-1929, Theatre Guild
1928-1929, Theatre Guild December 2013, Arts Building, Rotunda
2017, National Youth Theatre


Heat Wave is a 1982 studio album by vibraphonist Cal Tjader and jazz singer Carmen McRae. Tjader died four months after the completion of Heat Wave, it was his final recording. The album was arranged by pianists Mark Levine and Marshall Otwell. McRae and Tjader did not get on well during the recording, and Tjader later overdubbed his parts without McRae present. McRae chose to sing "Besame Mucho" in its original Spanish language lyrics, and Willie Bobo helped her with the pronunciation.
Heat Wave peaked at 25 on Billboard's Jazz Albums chart.


text:
VIDEO: Financial Services Committee Member Rashida Tlaib Unsure Who Ben Bernanke Is
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POWELL: "Well, to — yes, to financial institutions, we do."
TLAIB: "So when the feds step in to rescue banks in a crisis, is that because you believe their role in the economy is vital?"
POWELL: "Really, because we had no choice to prevent the financial system from collapsing in 2007 and 2008."
TLAIB: "My city filing for bankruptcy was devastating to so many retirees, sir. 40, 50 years they worked for the city of Detroit, saw their pensions completely diminished, gone. Do you not believe that the governments of Detroit and Puerto Rico also play a vital role that should be preserved even if a financial crisis makes it hard for them to borrow money?"
POWELL: "What I believe is that it's not a job for the Fed. The Fed has a particular role and particular authorities, and lending to state and local governments, and supporting them when they're in bankruptcy is not part of our mandate."
TLAIB: "We're going to strongly disagree. I do believe you do have the authority. Now, you've mentioned that in the face of another financial crisis, you would use the same tools of expanding the balance sheet and purchasing long-term bonds, in other words, more of the same, correct?"
POWELL: "Yes."
TLAIB: "So I'm afraid that's simply not good enough. I think your predecessors, former chairs Yellen, and I believe it's Bernanake, seemed to agree based on remarks both gave last month. For instance, Chairman [Bernanke] has suggested a money finance fiscal program might be helpful during the next recession. Do you agree with that would be helpful?
POWELL: "I think that's really an untested and not widely supported perspective. I don't believe Chair Bernanke said that a money supported fiscal policy would be something that we should do. I know there's been a group of people that have pushed that idea, but I don't think it included former chairman Bernanke. You may see something I haven't seen."
TLAIB: "I know,. And Chairman, look, the federal government is supposed to be about people. I don't see that we're treating pensioners, a city like the city of Detroit which is front line communities that have really been hit hard by the financial recession. They keep saying Detroit's coming back. If I show you neighborhoods, they'll tell you, we don't know what you're talking about, because poverty's actually increased, access to housing has decreased."
It Took 5 Years – But This Marine Vet Amputee Didn't Let Anything Get In His Way
Church Creates Black Lives Matter Nativity Scene For Christmas


text:
EVENING GOLF
VISITOR BOOKINGS
LADIES GIVE GOLF A GO
CORPORATE & GROUP
SEVENS GOLF
FUNCTION CENTRE
2020 MEMBERS SURVEY
Irrigation Upgrade project
History of The Vines
The Club was established on the 16th April, 1925 as a 9 hole links course on a farm property in the southern Adelaide suburb of Marino. It was originally named The Marino Golf Club.
In 1963 the club purchased land at Reynella to establish an 18 hole course and, as a great tribute to the dedication and hard work of the members at that time, the first official competition was held on 20th June 1964. The new Clubhouse was completed in the same year.
The early years of the club at its new location were particularly difficult with the extreme heat of Adelaide summers and a limited water supply creating challenging conditions for the establishment of turf. A dam of 40 megalitres established at the southern end of the course, and a small bore, were together not sufficient to sustain the course and from its earliest years the quest to establish irrigation self sufficiency became the single highest priority of the club.
During the 1980s three small dams were built in creeks on the course and in 1994 the first of a series of major initiatives was undertaken. With the support and assistance of Onkaparinga Council a small stormwater harvesting pond was established in the nearby Panalatinga Creek with a pipeline of 1.3km delivering pumped water up to the club's main dam. The start of a new era for the club had been made.
With a significant improvement in water availability the installation of an automatic irrigation system soon followed, and this was the catalyst in 1996 for commencement of the complete re-turfing of the golf course. Drought resistant and tough Santa Ana couch was selected for the fairways and over a period of six years this work was completed to produce what is now one of the finest playing surfaces for golfers anywhere in the state.
Confusion about the location of the club prompted a change of name in 1998. The Marino club was now at Reynella and, with its location in the birthplace of the wine industry in South Australia, the club adopted its new name of The Vines Golf Club of Reynella.
In 1999 the club received a windfall. An offer of 100,000 cubic metres of good soil that would be delivered, bull-dozed into place, and graded to a finished surface anywhere on the course that the club required. Steep slopes were shallowed, valleys were moderated, sweeping mounds were created and holes re-designed to create the outstanding layout that every golfer who visits the club now enjoys.
The early years of the new millennium saw a new focus on the club's drive for water self sufficiency. Reliance on mains water at summer's end could not be sustained and so began 10 years of new projects, each in turn adding its part towards the ultimate goal. In 2001 the water harvesting pond was increased four-fold with a significant increase in pumping time and yield from the creek. This in turn led to a proposal to increase storage on the golf course and in 2005 construction commenced on a new 45 megalitre dam on the club's property.
The new dam was part of a much wider project of irrigation management with approval and establishment of a recharge capability for the club's small bore, and a substantial upgrade to the pumping system to increase the yield from stormwater harvesting. This project was completed in 2006.
In 2007 the computer driven irrigation control system was fully upgraded and in 2008 negotiations commenced with state and local government authorities for approval to capture stormwater from a drain running around the perimeter of one of Adelaide's major reservoirs. This project was approved and completed in 2009 with the first pumping of water on 13th July of that year and all five dams on the club's property full for the first time ever by the end of August. With this project the irrigation water future of the club had finally been secured. Total cost of the combined projects over the 10 year period exceeded $600,000.
In 2017 the club embarked on an ambitious renovation of the catering and bar facilities in the clubhouse, with new kitchen facilities, bar and coolroom, an update of chairs and extensive re-carpeting to make the member's facilities safer and more efficient. With these improvements the club should be in a sound position going forward with it's hospitality for members and guests.
Nesting boxes high in the trees have encouraged an abundance of bird life on the golf course with the colourful Adelaide and Eastern rosellas very prominent. A family of tawny frogmouths, totally immobile in daylight, visit the same tree at the course each year, and the delightful blue wren is a common sight in the native bushland. Koalas are regular visitors.
The Vines Golf Club of Reynella has a history of strong volunteer involvement by members in the development of the course which has contributed to its enviable reputation as "the friendly club". The Clubhouse is roomy and comfortable, but the great pride of the club is the course itself, with rolling tree lined fairways of fine turf, wonderful greens that are fast and true, and an environment of natural beauty that is a joy for all who play the game.
If you would like to call us:
Office: (08) 8381 1822 Pro Shop: (08) 8381 3300
If you would like to write to us:
The Vines Golf Club of Reynella SA Inc
PO Box 243 HAPPY VALLEY SA 5159
info@vinesgolfclub.com.au
If you would like to visit us:
We are right at the East end of Reynell Road:
Cnr Reynell and Pine Roads HAPPY VALLEY SA 5159 (or Cnr Reynell Road & Mark Street, Happy Valley)
Or fill out the form below to send us an email:
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The Vines Golf Club of Reynella


text:
Seattle City Council approves $25 gun tax, regulations
By Dyer Oxley
As the Seattle City Council prepares to take a vote on a proposed set of taxes on firearms and ammunition, local business owners are also preparing for a lawsuit should it pass. (AP)
In a vote on Monday afternoon, the Seattle City Council unanimously approved a new gun tax and regulations within the city.
The new tax places an extra $25 on each firearm sold within the city and 5 cents on each round of ammunition. The council also passed an ordinance that requires mandatory reporting of lost and stolen firearms.
Related: John Curley says Seattle gun tax won't stop gun violence
"Every day, the general public pays the enormous cost of gun violence," said Council President Tim Burgess moments after the vote. "Gun violence is a public health crisis in our city and our nation. City government can and must pursue innovative gun safety measures that save lives and save money. As it has in other areas of policy, Seattle can lead the way in local solutions.
"A gun violence tax will give us revenue to provide broad-based benefits through research and prevention programs. Mandatory reporting provides the police information critical to investigations. I'm grateful for my colleagues' full support for both of these measures."
Critics have called foul on the arguments for the taxes.
Sergey Solyanik is one such critic. The owner of Precise Shooter on Aurora Avenue, argues against the new tax, and has published his own numbers and figures that contradict the city's assertions it can take in between $300,000 and $500,000 in gun-tax revenue to put towards gun-violence prevention programs.
"Basically, what the city has done is that they have invented numbers," he said. "Pretty much, all the numbers they have associated with this proposal are outright fake."
Solyanik punched his own numbers, based on actual sales in the city, and he estimates Seattle would more likely get around $80,000 from the tax. But that is only if the tax doesn't alter the market. The gun store owner also argues that the tax will cause customers to divert their purchases to shops just beyond the city's border.
"My store will definitely have to move. It would not be economically viable to stay in the city," he said, noting that there are only two primary gun shops in Seattle, but many more just outside the city.
But that move won't come immediately. The city can expect a legal battle over the taxes first.
"The very first thing that is going to happen is that all the gun stores will have to file a lawsuit trying to stay this," Solyanik said. "So while the court considers it, the law won't go into effect."
Solyanik said that the cheapest option is for him to sue the city. The next cheapest option is to move his store. If he stays in the city, he will go out of business under the taxes.
For example, Solyanik said that his gun store grossed $22,000 during the first six months of 2015. That is before he pays expenses. He estimates that if he paid the city's taxes himself, instead of passing them along to the customer, it would cost $23,500.
If business owners made good on the threat to sue Seattle over the gun taxes, word of the controversy will likely make its way to the state's attorney general's office, where it might face state-level scrutiny.
"A plaintiff could sue the city, probably for a declaratory judgment declaring the city ordinance invalid as being preempted by state law," according to Alison Dempsy-Hall with Washington's Office of the Attorney General. "The Declaratory Judgments Act requires that any party alleging that a local ordinance is invalid give notice of that claim to the attorney general."
Solyanik believes Seattle's ordinance is invalid.
"The only real intent for this law is to regulate the firearm industry, basically regulate it out of town," Solyanik said.
He further said that the state's own laws prevent cities from making local gun control regulations.
"There is a law in the state of Washington that says you cannot intact more restrictions on firearms than already exist in the state," Solyanik said. "It is extremely easy to show that the only intent with this law is not collecting the revenue, but legislating on guns, and it should be easy for a court to see that this is illegal."


text:
Strange earthquakes in Texas, Ohio, and elsewhere… who's to blame?
27 April 2010 Richard creepy, science
Welp, here we are again – more earthquakes in regions that don't typically experience them. A "rare" 4.0 earthquake hit South Texas on Saturday, April 24. The area, it seems,
does not experience these types of quakes.
The depth of this quake was around 5.0km, which
suggests it was either geological or induced by oil production, which
does happen. Never-the-less this is an interesting area to have a quake
due to the fact the area has a large crack running north to south in
Texas, discovered several years ago, origin being unknown.
Meanwhile, a smaller quake rattled Northeast Ohio the same weekend.While Ohio has seen a few small earthquakes before, "it's not clear what causes them."
Why the quakes in strange places? Some scientists have speculated, as we have reported before, that the Haiti earthquake in January forewarned increased seismic activity in the Americas. An Iranian cleric, though, offers an alternative explanation:
A senior Iranian cleric says women who wear revealing clothing and
behave promiscuously are to blame for earthquakes.
Iran is one of the world's most
earthquake-prone countries, and the cleric's unusual explanation for why
the earth shakes follows a prediction by the president, Mahmoud Ahmadinejad,
that a quake is certain to hit Tehran and that many of its 12 million
inhabitants should relocate.
"Many women who do not dress modestly … lead young men astray, corrupt
their chastity and spread adultery in society, which increases
earthquakes," Hojatoleslam Kazem Sedighi was quoted as saying by Iranian
media. Women in the Islamic Republic are required by law to cover from
head to toe, but many, especially the young, ignore some of the more
strict codes and wear tight coats and scarves pulled back that show much
of the hair. "What can we do to avoid being buried under the rubble?"
Sedighi asked during a prayer sermon last week. "There is no other
solution but to take refuge in religion and to adapt our lives to
Islam's moral codes." Seismologists have warned for at least two decades
that it is likely the sprawling capital will be struck by a
catastrophic quake in the near future. Some experts have even suggested
Iran should move its capital to a less seismically active location.
Tehran straddles scores of fault lines, including one more than 50 miles
long, though it has not suffered a major quake since 1830.
There probably are more "immodest" women in Ohio and Texas, to be sure, but I can't help but feel that this explanation leaves something to be desired.
Giant bacteria colony "the size of Greece" found in ocean
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